{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Embedding analysis\n",
    "\n",
    "In this notebook we visualize the embeddings learned by the encoder. Run this notebook after training a model via the `encoder.train` module.\n",
    "\n",
    "We start by importing needed packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "sys.path.insert(0, os.path.abspath(\"..\"))\n",
    "\n",
    "import argparse\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.optim as optim\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch_geometric.data import DataLoader\n",
    "import torch_geometric.utils as pyg_utils\n",
    "\n",
    "import torch_geometric.nn as pyg_nn\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    "from sklearn.manifold import TSNE\n",
    "from collections import defaultdict\n",
    "from itertools import permutations\n",
    "from queue import PriorityQueue\n",
    "import matplotlib.colors as mcolors\n",
    "import networkx as nx\n",
    "import pickle\n",
    "import torch.multiprocessing as mp\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from common import data\n",
    "from common import models\n",
    "from common import utils\n",
    "from subgraph_matching.config import parse_encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we load the model and a dataset to analyze embeddings on, here ENZYMES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "142it [00:00, 1417.27it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset otf-syn\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "600it [00:00, 1210.18it/s]\n"
     ]
    }
   ],
   "source": [
    "parser = argparse.ArgumentParser()\n",
    "\n",
    "utils.parse_optimizer(parser)\n",
    "parse_encoder(parser)\n",
    "args = parser.parse_args(\"\")\n",
    "args.model_path = os.path.join(\"..\", args.model_path)\n",
    "\n",
    "print(\"Using dataset {}\".format(args.dataset))\n",
    "model = models.OrderEmbedder(1, args.hidden_dim, args)\n",
    "model.to(utils.get_device())\n",
    "model.eval()\n",
    "model.load_state_dict(torch.load(args.model_path,\n",
    "    map_location=utils.get_device()))\n",
    "\n",
    "train, test, task = data.load_dataset(\"enzymes\")\n",
    "motifs = []\n",
    "for i in range(1000):\n",
    "    graph, neigh = utils.sample_neigh(train, random.randint(3, 29))\n",
    "    motifs.append(graph.subgraph(neigh))\n",
    "\n",
    "batch = utils.batch_nx_graphs(motifs)\n",
    "embs = model.emb_model(batch).detach().cpu().numpy()\n",
    "\n",
    "max_n_edges = max([len(m.edges) for m in motifs])\n",
    "max_n_nodes = max([len(m) for m in motifs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the learned order embeddings in 2-D space. We'll use a PCA visualization so that straight lines are preserved. This property implies that all points which are subgraphs of a given point fit inside of a quadrilateral-shaped region. We color the graphs by the number of edges contained in them.\n",
    "\n",
    "We see that the model learns to organize the space by graph size, which makes sense since bigger graphs cannot be subgraphs of smaller graphs. The model also seems to pick up on some interesting global structure (some points are quite far away from the main sequence)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j48fSP0XlIFLOPmflkFQp5QxgBgTSXLRycRSFcd26MK5bl9YuhqKcsubqgSoCwq2VePzJv7DGfvEitJG19n6Kcs7zGQY7c3LJLi1r7aIoP2DNVVPYAdwghIis1a/QH/Bxog9hB+AEehDcr9C/6vvOZiqfopxR5m7ZzjPfLgXAME0Gt0/l5SnXkBCpJp0pLau5agrzADtw0/ENQggduAX4pmrkEcACAqOUptc6/nZguxp5pJzNyr1efjP/Wwb/7WX6P/ci93/4KZlFxSH7rcs8wv99s5gKn48Knw+vabLpaBY/mfN5K5Ra+aE7qZqCEOL4YrMjqr5fKYTIA/KklEullJuEEB8C/xJC2AmMIPox0I0aAUBKmSuE+AfwlBCiDNhIIHBMIjCsVVGqHTpawNxvNnMsr4RRA7twzYSBREWemWsGSCm5e/ZcduXm4TcDo7hXpGdw09vv8+2P7yHW5are939rNuLxB09+MyyLbVk53Pz2+xS5PYzp0okfjxtN+zg1EU1pXifbfFR70tqrVd+XAhOqfr4H+DPwDBAPbAGukFJurHXsb4By4FEgFdgD3CylVJm8lGorN6bz6399gWGYWBI27jjMB/M38PZfbic+NrK1ixdi89Es9ucXVgcECKyv4PYbfLJ1J3ePHl69PbssfB+C37LYfCwbgCPFJXy9ey+f33c7HVRgUJrRSTUfSSlFHV8TauzjllL+UkqZKqV0SSnPk1IuCXMuU0r5jJSyi5TSKaUcLKX8+OTfknKuyThayBMvfIbPHwgIAB6fQWFJBe9+vrZ1C1eHAwWFSEIHxnkMg105eUHbLuzepcEZzqaUVHh9/GflmtNaTkWpTc1/V85oUkoefe5jwq0Q6Dcslq2vPe/xzNAjMYFw2Y9cuk6/lLZB2+4eNZxYlxN7A+koTClJO3T4dBZTUUKooKCc0Q5k5lNc6q7z9TO1T2Foh3b0TEoIqgFoQuCy69w4uH/QvglRkcy7/w7uGDmUHkkJDO/Qrs6lO1Niopu13IqigoJyRqvw+NBt4f9MhRDcfMXwsK+1NiEE79w2hWsH9sWl69g0wbhunfn47mlBnczHJUZF8qtLxvP1g3fxwV23MqFHt5AmpQi7zoPnj2qpt6D8QJ2VM5qVH46+3VLCNh0BDOrVjqsu6h/2tTNBtNPJXyZfxl8mX9bkY/927RU8/sXXrEjPwF4VFB+bcAETTmPKbUUJRwUF5YzmdOj8v/sv5S8zvsHnN5AS7LpGh+R4Xvz11GbJONpSMouKeXP1erZn59K7bRIPnD+S7omByfzRTgev3XQdBRWVFFRU0iUhHqeuPq5K8xN1PYWdLUaOHCnXr1/f2sVQmtn+zDw+/W4LBcUVjBvWncvG9cPpOHtvkrty8pj27of4TAPDktiEwKHbeOe2qQzt0K61i6f8AAghNkgpR4ZsV0FBUVre9JkfsS7zaMj2/ilt+ey+21uhRMoPTV1BQXU0K0or2Hw0K+z2XTl5mHWs5KYoLUEFBUVpBdEOR9jtLruOdhb3kyhnPxUUFKUV3D5yKK5aHccuXeeWoYPO6s5z5ex39vbUKUozWLYjnbcXb6CgrIIL+nXj3otHkhgTddqv8+Nx53G0uJQvd+7BqdvwGSYTe3Xn8YkXnPZrKUpTqI5mRany1qJ1/Gfhajy+QMZSu00jNtLF3CfvICG6eZLu5ZVXcKiwiE5t4klVs5WVFqQ6mhWlHhUeH68uOBEQAPymRZnby3tLaif2PX3aRkcxqnNHFRCUM4ZqPlIUYF9WPnabhtcfvN1nmKTtyeDRq5vWrDN/5x7eWL2egopKxnXrzCMXnt+klNd5lRV8kb6LArebse07M659Z9XXoLQIFRQUBUiMiQxa+6CmlPiYJp3r1RVreD1tLe6qhXM+27aL7/YeYN4DdzaqRrDyWAb3ffMplrTwmiZv7djAyJQO/O/yG7Fr9afYVpRTpZqPFAXolBRP/47JIcn3XHadOyc0PuleudfHa6tOBASoWgvB5+eNtHUNHm9YFg9//wVuw4/XNAGoNPysyznKp/vVkuVK81NBQWkx2/Ye4/7fzeKiO/7FNT95jQ++3lBnsrvW8OJ91zK0a3scuo0op4Mol4OnpkxkRI+OjT7HgYLCsFldDctibeaRBo/fmp+NP8zkNbfhZ86e7Y0uh6KcLNV8pLSIPQdz+Nmf51R35OYXVfD6hysoLqnkoVsvbOXSBcRHRfC/R24iu7iMkgoP3VLa4KgjCZ1hWazJOEyx28Oozh1Ijg40C6VER+GvesKvSQAd4+IaLINNiDDrtVW9pp1cn0K5z8fmvGPEOV0MTExRfRNKvVRQUFrE/z5Jw1trcXqP1+CDrzdy1/VjiHDZW6lkoVLjY0itpx9hX14+d82eW9VEJPGbFvePGcnPx48lNTaG0Z07sfpQZtATv00I7j2v4WaoQUmpROl2Kvy+oO0um073uAQWHNrLhI7dcOmN+329s3Mjf1mzFLumYUpJcmQU714xFY/0MuvgWrIqS7gwpSdTOg8nyn5mLliktCzVfKS0iH0ZeYRrKbJpgpyC0pYv0EmypOSBDz8jv6KSCp+PCp8fn2ny1toNLDtwCIC/X3cFLnvw85bQBP9d3fB8Gk0IZlx6PdF2B5G6HYdmQxcaPtPgk307eGzp14yc9SobckKT6dW2LvsIz65disc0KPP7qDT8ZJaWcPPCd7lt+Zt8mrmJVXkHeHHn99yw5D+U+Ope4U754VBBQWkRXdonhN1umBZtE5o2uqc1bc/KodjjCdnu9hu8v3ELAGsyjmBawRHQb1qszjjMtqzsBq8xLLk9a6b9mGfGXcK0voOxaQILcJsG5X4fZX4f9yz8BF+YZqqa3t25CY8RXDuzsCjTi/GYfqyqKO2xDPI8Zbx7IK3BsinnPhUUlBZx35TzQ9Y/cDl0rps0mKiI8MnhTifDtJi1dBPXPfcOV/7pTf45bzml7tCbe0Mqff46E9aVeQNNPusPH6XS7w953ZKSzUcbDgoQSJg3pddAyn2+sDd/U1qszsqs9xwFnsqQ/gnNZiFEaJXNZ5l8l7WrUWVTzm0qKCgtYmCv9vz1sevo3K4NAoiKcDBt8kgevXNCi1z/iXe/4sWvVnAwp5CjhaXMWraJ6f/8IKSfoyFDOqSG1AIAInSdyf17A9AuNganHjqfQNdsJEc3LY9SpeGvs+PZY9Zf9su69MJlCw7EUtbdkR1rj2hS2ZRzkwoK54iD6bk89uhMLp/0HNdN/jtvvL4Iv7/+5oWWdt7grnz4j3tZNvMXfPvmIzx48zhsWuP+BItKK9lzMIcKt6/hnWvZl5XPil2H8NQIAD7DJLeknIWb9zbpXBF2O3+88mJc+okU15F2Oz3bJnLj4AEAXD+wH3qt9yUIZEGd2Kt7k653Tfe+RIbpVDYsizHtOtd77C19BtE5Ji4oMLg0B6nOOGy1ajsRNju39zivSWVTzk1q9NE5ICenhEcffpfKysANs6Lcy2dz15N1tJjf//HGkP0rKrxs3ZyJw6kzZGhn9DBPtc0p3Dj+uvj8Bs+8tpCl6/Zh120YpsVtk0fywE1jGz20cntmNuH2dPv8rN9/hGtH9W90eQCuG9iPfslt+XDTVgoq3Uzs1Z0r+/XGYQv8HhOiInlr2hR+8dl8CioqkUi6JrThpRuurt6nsa7o2ouP9m5jbfYRKg0/NiGwazb+7/yLiXXUP1ooQrfz+XW38+HebSw4uI+EiAju7j+cLvGxPLDqPbLdpWhC4LNMpncfzWXtmvZ7UM5NKiicAz6Zsw6fL7gpwes1WJ22j5zsElJST4yPXzB/Cy/9a2H1jdmma/z5uVvoP6BDi5a5sf75zmKWrduPz2/iq6r5vD9/Pe3axnLNxEGNOkdyXHTYMf4O3UaHxMbnI6qpd3ISv7t8UtC2Hdk5fLN7P7pN46p+fVj0k3s5XFyCrmm0b0Leo5psmsZbl09h8eF0Fh7ah8umE+Vw8P3hA6SXFnJ7v6F0jomv8/hIu4N7BozgngEjgrZ/MelhdhQfI99bzsD4DiS5VEI+JUAFhXPA3r1ZGEboLFi7XSczs6A6KGQcyuPlfy3E5zWo2Qjz1JMfMOeTR3E4z6w/B5/fYP6yHdXB4DiP1+C9L9Y2OiiM6d2Z2EgXbp9RPeIGAjfcG84beFrK+tz3S5m9YSs+w0DTBDPS1vP4xHHcNSp0bkKJ28MHm7ayOuMwXdrEc/vIofRMSqzz3JoQXNy5B0PapnL1Z+9R7HHjNg3smsa7OzfzzuVTOK9dpyaVVwjBwDbN9yCQUV7AX7Z9zdr8g7hsdqZ0Hs7P+k3CYTuz/saUUM3WpyCEmCCEkGG+imvt10YI8V8hRL4QokII8Z0QonGfdgWAnr1S0PXQ/0q/36RjpxNDQb+evxXDCO1nkJZk3doDzVa+ikovL89cyjU/eY3rHpnBjI9W4PGFjs45zrIk6YfzOZCZH3ZuA0BRaePH1Ns0jbceuZkBnVJw6Dacdp32CbG8/tCNJMed+hPy9qwcZm/YiscwsADDkngNgxcWrSC7rDxo3/zyCq56411eWbGalQcz+XDTNqa8NZvl6YcavM6LG1eR767AXdXB7Lcs3Iafx5Z+fcrpQixpsShrN0+s/5jfbfqcjQX1j2yqT76nnFuXvcHK3P34LJNSv4fZB9fy83UfnlIZlZbREmH7Z0DNTGDV7Rwi0Cg8D+gK/BQoAp4CFgshhkopG04Wo3Dj1NEs+GpLUG3B4dAZMaob7dqdaFooL/NgmqE3D0tKKiqa3oHbGIZp8eDTH3A4qwh/VUCa9eV61u84zOtP3xrSL7Bx52F+//JXVLp9WFJihckDJIDBfdo3qRztE2KZ9Ytp5JdW4DNM2rWJwZKSvcfyceg2urSNP+n0Dwt278NrhI4EEkKweN8Bpg0fUr3t3yvWUFTpxqh6X6aUuP0Gv/7yG5b99IF6y/BNxv6weZFy3eVkV5bTLurk5ntIKfnFuo9YmXsAt+lHAPOPbufenmN5uO/EJp/vo0Pr8ZrBo6a8lsHqvIMcLMunW0zSSZVTaRktERR2SSlX1/HatcA4YJKUcjGAECINOAg8SSCgKA1o1y6ef7x0By/9cwG7dx3D6bRzxeQhPPij4DbvsRf0YsninXjcwU/plmkxfETXZinbig0HyMorqQ4IAD6/yf6MPDbtOsLw/oFmj93pOXy2aAtfLtmBWSOFde1bpKYJXA6dh6dd1KRylFZ6+OtnS/h28z4MyyIhOoKSSg8gEEC7NjH8675r6ZYcfpJdXQorKvl2z/6wwzyFIGR01aL96dUBoaYSj5ejJaV0jI/DsKyQ0UsQGOUUjmVJ9hTmYVoWHWMazq9U26q8A6yqCggAEvCYft7ct5IbuwynXUTTzrmt+CheK7RGqms29pflqqBwhmvtBr5rgWPHAwKAlLJECDEPuA4VFBqtV+9UXv7P3ViWRAjCPnGeN6YnAwd2Yvu2w3g8foQAp9POzdPGkNT29Mwqzs4vZfmGAwgBF43oyc4D2bg9oU1FfsNkV3o2w/t3YsZHK5n91Xq8vtCnbQm4nDrdOiRS6fEzoGcqd98whk6pbeosQ0mFh7mrt7EtI4ue7ZKYMmYQP5nxKRl5RdVrJuSWVAQdcyiviHtfmcM3f7gfeyNHCFlSctvMOWQWFdf5+iW9egRtCzd/AcC0LBbtO8CMtPXkllfQNiqSn48fy01DT7Sk3tl/GM+tXYqnxmQ2ARjS4pFF8/BZJqNTO/Kfi68jpoGRSTUtytpDpRn6f6QJwarcA0zp0vjU4QB9Y1NJy0vHXyswmNKiW7QKCGe6lggKs4QQSUAxsBD4lZTyeIPlACBcPuAdwJ1CiGgpZXmY15U6aPVk0rTZNP7815tZtnQ3S77fiSvCzuRrhjF4SP3j3Rtr7jebeGnmUiAQlF6euZQJo3vhcup4vME3fIddJzUplkNHC+oMCNUkXDtpMNdfPLjBMhwrLGXaP2bj9vnx+A2W7zrEO4s3ANS5iA6AlODxGazancH4AY2bS5B2KJOcsjLMMO35uqbx7OTLSIg6sbazrFpXIZykqEheWLKyei5FXkUlz3y7BE1oTBkygDKfl025Wfiq3oMGaELDlBYSKKtKoLcm6wiPLZ3PjBUEfEAAACAASURBVEtvaLD8FX4vnx7exOq8dASE1HaEEETqJ2abW9JiRe5+vs/aTYzu4vrOQ+kZmxxy3lu7jWLWwTUhQcEhbCpD61mgOYNCCfB3YClQCgwDfg2kCSGGSSlzgQTgUJhjC6u+twFUUDiNbDaNiZP6M3HS6R2TfjSnmJdmLg0ZKbR4zV4cDj3opqMJQYTLzoUjejBn4aag5qJwJDC4d+P6EP7+xTJKKj3Vo4x8Vc1WjbkVmZbF5oNHiY9yMaBTaoPzKTIKi8PObga4pn9frh7QN2jbgYJCyr3esPvnV1aGBC233+DFZauYMmQA9yycy5a8bKyq36IFSGmF3Mh9lsniwwcp8XqIc7rqLHuBt5yblsygxO/GE6aWAICE8SmBWdqmtPjpmg9Yl3+IStOHDcH7B9fy68FXhdQkUiJiee+Ce7l7xduUGW6EkFhSUGZ4uG3Zf5k36WGSI04M0fWYXor9pSQ44nFoZ0623B+qZgsKUspNwKYam5YKIZYBawk0C/32ZM8thHgQeBCgc+fT85SrnJrFa/fVMQJGcP3FQ1i3LYODR/KRQN9uKTz98FU47Dp23Rao3dQx+drl1LlwRE+6d2pcs8PK3YeChp0e15ixOR6/wexlW/hgxVbsusY/7r6GYd3b89qqtby7bjNlXi+D26fy20snMLBdCr2Tk8LmQYq02xnZOXS4Z4XXh02EDzR11WJyysrZWZDL9oIcfLWevOtbd6HU5603KPx79xIKvOUYMvi6AojUnQjglfOmVdcUFmXtZm3+wep+BxOJaRn8eet8Lm3fLyRFhiYgOiKXVKcHAfgtjZyyGHymLTASqf8lmNLivUOfsjBnGRoaEskNHS5jascrVY2iFbVon4KUcqMQYi8wqmpTEYHaQG0JNV4Pd54ZwAyAkSNHnjlLd/2AWZZVx/BRSZvYCN559g6KSysRmiAu+sQNZMLoXvx79rKw5+zZOYmbrxjO5PEn5hJIKdlzLA+Pz2BApxTstdroXXadSm/dw10b4j6eyM4LD7/xGePH9mDBnv3V2UY3HjnG9Jkf8em90xnRsT292iayKyevOmmdTQhiXE4m9+8Tcu5+KW3D3sidug2XrlPiCa1FJMdF8cbWdRh11EjCidQdtG9gJNKirN0hASFQfo3fDr6Ky9v3x2k78dS+4OiO6oBQk12zsTbvEJe07xe0/YU9/yHG6eH4vd1hs+gQV0JGscaO4iwA5hyezzc5y/FZJ8776dFviLFHc0Vq0wYSKKdPa+U+Ov4XvoNAv0Jt/YFM1Z9w9hg/qlfY/gxNaIwf1QuA+NjIoIAAkJwQw1MPXIbDbiPCZSfCacdht/Gbhy7nvb/exTUTB1Wfd39WPlc98z/ufukjfjzjU8b/7jUWbdsfdL4pYwbhrLWWgd2mcVH/bnVmN7VpAnuYpiIpJV9v3BOSftpnmMxIW4cQgrdvm8ItwwYR63QSabdzZb/ezL1nGpGO0GYQh67zzFWX4NL16txDEXadjnFx/ObSCbhqrfKmRUBWRBnz0neHtM9D4IbstNmwV41UEkCETeeZsZc0mFOqrkV6hBBMSO0dFBAAXDZ72CY4CThrTUj7NnsF+f48av+6BdA20k2/+FSklHyZtQivFTwU2mv5+PTIwnrLrjSvFq0pCCFGAn2Aj6s2fQHcI4QYL6VcWrVPLHANMLsly6acGk0T6DYtqE9B0wT33jiGjil1p2EAuOLC/owZ0pWVm9KREsYN706b2Migffymyf2vzqWwvDJo+6/e+5o5T9xOl7aBCuePLj+PPcfyWLPvMLomsCT0SE3gx1eMYf3+I1SG6eiNcNjD1i68fgNbhI3abVumlOzMzgUgyuHgd5dN5HeXNW48/+T+feiRlMD7G7aQXVbBhJ5duX5QfyLsdiLsdv6+ZAVHiktpFxdDprM47LyE6t+JZfLUqIvIc1eQlnWYzjHx/GjwKIYlN9z/Mq3baF7a+T0e60TA04XGyMQuYbOlTukynAXHdoT0P1hSMjqpa9C2uUcWhL2mEOCw+bmt22gMaeAxw/evlPjLGiy/0nyaLSgIIWYRmG+wkcDIo2EEJqYdBV6q2u0LIA2YKYR4ghOT1wTwt+Yq29nk4O4sFn60hvISN+dfNpAxlwzA1oSEcs3F4/GTtnIvRUUVDBrcid/PWIC71o1Vt2mktm1czp/42MigZqKavlm5i5c/WUGRWRnSY2yYFp+s3s4vrgms8+zQdV554HrScwrYeyyfzknx9OuYjGFaIU+uEOj07t8phW0ZWbhrjYBy2nUq9dCbsiYEfZKTkFLit6wmJ7nrm9yW/7vykpDtl/ftxeV9A7Wq7zL28+iSr0L6EWp7cVMaG29/mIhGLs953O3dz2Nb0REWZe1B1zSkhHaRcfx1RGgCRYDhiZ25v9cFvL5nGX55okyWtHh4zfu8OuY2HFrgdlJihL+pSwmJjijezfiAK1LH09aZQK63IGQ/Q5pku/NIjWjbpPeknB7NWVPYDkwjMFM5EsgGPgH+IKXMB5BSWkKIq4EXgFcBF4EgMVFKebgZy3ZW+PqD1bz+x8/x+wwsS7Jy4Tb6DuvCM2/dj60ZMpsWFJQz7/ONZBzMo2+/9lx59VBiY0OfGg/sz+Hxn8/CMCwMw0QIgUeXyDgbNe+8Pr/J3G82c9m4fiHnaKxZ89bx37mrKBMGMlZQ+85uWBb5pRUhx3VPSaR7yol8QnbdxiNXjuXF+SvxVN38BYE+iF/dMIFXF6Sxcveh6sAQ4bAztk8X9ESdhTX6FAAcNo1op4OR/3iVcq+PjvFx/PbSCU1Oi12fxqYUtwnBhpxjXNChS9POLzReGHkTGeUFbC8+RvvIOIa26VRvB++P+4zny8NbOVRx4kbutQw2FWYyO30td/ccC0CHiBQOVoQmIxACyqxS1hRuYU3hFrpEhM+9JJE8vuVZ3hr9N3Q1jLXFNefoo2eBZxuxXyFwb9XXD5rX40fXNWy6jYpSN6/98XN8NSZ+eSp97N6UwfKvtzLhmmGn9dr79+Xwy5+9h98w8ftM1q45wEcfrOHVGfcEZVmVUvL733xMWVnwqmXCD7pDYEQGB6vatYem8PkN3vwkDY/XwGYj7LjSCIedC/p1a9T5po8fTtu4aN74di15peX07ZDM1PMH0SkpnufvmsyCTXv4dM0OkJLrzxvIlcP7YElJ25hoZm/cgtvnp09yEj2TEvlk607cVXMKDheX8OinX/HfW25gdJeOJ/1+azq/gbUSjrMgpC+iKbpEJ9Il+kTwXHB0O2/sXUGBt5xRSV35ab9JdI4KjPs4VllMlrsk5Bwe02BuxsbqoHBHlxt5bvd/gjqQw8lwH0VDwyK0Nua2PNy6+mdE2FxMTp3AzZ0nYxMtm+L9h6q1ZzQrwO7NGbz064/J2JuNTbcx8bphjLioD7puw0fwB8tT6WPZV1tOOSh4PH7S9+cQGxdBx06J/OP5r6rXY4BA6m2/3+T1/3zP7//vRJNCxqF8SoorQ84nJOgVZlBQcNptXHJ+35B9Gys7v7T6Z80EvRKMCBkY70jgKb9HSgKXDOnZ6HNeNrQ34/p25f+9N5/VezPZcigLS0p+Nnkc0y8axuQRwbUaG/DkpAt5YuIFSAKdzKP/+Z+QzmePYfDS8jRmdrnppN9vTS5d5/VLruP+bz8DJH7TCjtaKFK3M6xtu9NyzRl7lzNj77LqUUYLju5gWc4+/jX6ZgSCGL3uWdKWlJjSYmn2XhZn76GHayQl1kHyfQWY0sKU4ZvBwgWEmtymh3lZiyg1KvhRj2kn/+aURlNBoZUdy8jnqemv46laUczyGSz+fBPpu7IINxJdCIiMOrU1jed9vpHXX/0ezaZhGiYdOyeSvj8nZD/Lkqxbkx60zTStOqvzmgh0Nhumhctpp2NKHDddfvLBKzEuKmhim6NMYvOBPxKiIh08fPUFTDl/YKPTUhz31KyvWb03E59hVk9ue+mrFXRIiCMlPhrLsujbMTmoCUeIQI6k/IqKsH0TAAcLw46gPmkXdOjKmmkPseDQPsp8HvYWFfDZgZ3YhBb4XWsab18+pdFNTfWpNHy8vndp0BKfFpJyw8uP0mYSYXPgNQ1cNjteq1bfi6ZzdcdBPJQ2i82FmVSafjRAFzZ+PfgOlhV8yTFvbtjrOoUDr6w/GaPX8rEoJ41xSSOI1iPpEtlBNSk1IxUUWtlnby3HX2udYL/PIHN/DnZ76M3O7tTpM7QLuzdn0GtQp3o7nd1uH9lZxSS1jSEmJtA3sG1LJq/9+zu8NdJOHDyQW2eKametNRa6dU/GFWHHXWtZTKdTZ+q0MbhdkFNQxpgh3bh4TG8c9pP/E4uKdHLZuH58u2o3Xp+BAHQvREkbz//kGkYNbFo7OkBheSWrdmdUB4Pj3D6Dx96eFwgwApy6zvN3TWZ0r+B1CpKjo6pS6IXq0/bEBLtKn5/DxSWkxEQTHxF+Etme3HyOlZbSPyWZlJjwKbzjnC5u6XMi/9Gjw8eSdiyTOKeLizp2DRkO6jEM1uccAQSjUzs2uhM8o7wAXdiokcS4mikDwQFAswROTQ/0I5l+Im0Ousck0S4yjk37V1bXMizAJ02e3vIll7fvAoQPClM7XcHszHnIBqYXGhj8ZderCCDGHs2v+j5E16jT01SnBFNBoZVl7M3GDLNAjm63Mf3Ry5j90rdVE8MkPq8BEt5+fj5SgjPCzh9m3EPfocE3Rykl772zgg9nr8ZmE/j9JhMv7s8vHr+Kj+esDQoIEKgRaJpACBH8ZO7QuerqoUH7aprgd0/fwK+f/BDLsvD5TCIi7PTomcL06WNxOOr/k7Isic9v4HTojXrae/K+S7DrNr5aGkiRFR3p5NE7JjY5IGTmFZORX4SuaYGhs2HWlfCbVvXM4kqvn5/+9zPm//Y+EmNODI916DoPnj+K19PWVvcpQKC559HxY5FS8vLy1fx39XpsmobfNLmqX2+emXxp9Q26qNLNAx9+yr78AmxaoCw3Dh7A01dMqnMuxXEdomOZ2jv8KK3Fh9N5ZNG86n8LIXjt4usa1Qnd1hXT4EgnCHQs945N4ZauI8l2lzAsoTMXpPTk52s/DDu5DWBx9hEGJ7WjXGYFbU9xtKOTqw//HDKYF/b+l6Pu7HpDw/E5DR5vIX/Y8S/eGPmsSovRDFRQaGV9h3Vh18ZD+H3BH0i/z2Dc5YOYPH0sG1fsIfdoMW8+9yU+jz8QHAB3hZff3DmDmWm/JyLqRHvvgq+38uH7q/HW6ORdungXEREOCgvCzwd0uewkJkWTl1uGpgWCw9BhXbj9rgtC9h08pDPvffATvv92OwX55QwZ1oVRo7vXW2uxLMk7n69m1rz1uL1+EuOi+Ont47l0bP19DnbdxpP3XcKjd0ygwu0jPiai3qR/x+WVlPPsJ4tZuiOQqloQ6JT2GyaNnRxsmhZfbdjFnROCl7L88bjRtIl08Z+VaymsdNMnOYlfXXwRQ9qnMmfzdt5csz6oz2HB7n1EOhw8fUUglfkT8xawMycvKIX259t30j+lLbcObzjpX9j3W1nBQ999HtT8A3D/t5+y+tYfEe8KHUVWU5IrmgtTerI8Z1+DwaHc7+HWbqOCtkXY6m7S9FkmhZVJPD3ser7NWcG+kiJ2FlVyxLKxPud9Yu0RvHre/UTqGjvK9vL2obkNdlIblsn6wm2MTWpaBlelYSootILcY4WUFFTQa1Anrr1zHF/NSsPwe6pzBzldds6/bCDJHQITssZcPIC5bywJm1vIsiRp325n0vUnblwfzFyFt1a6aq/XYP6Xm7l1+vkc2J+Dr1YQMk2LV167myOHizh2tJDuPZLp0rXuceJt2kQx9ebzGv2e35y7itlfra/OlppXVM6fX19IVISDscMaHsrpdOg4G6iFHOczDKb/6wPySsurE9ZJoKIqSNq0QHv88ZuyEIRtPvOZFi8uXsms3du4Y+RQbhsxBE0EalTThg8JWjznuBlp64JqEBBo0vlk6w5+fel43D4/qw8dDllTwe03eGfdppMOCvPSdxOuD8prGnywdxsPDR7d4DmeG34jf9j8Bd9l7UIg8FlGyBl1oXFRSq+QY2/qOoLvsnaF9Dccl+ctZ0TCINxGBB8f+BC3qQM+XLofpyuLX21/BiFAQwCCaFskbsuLDQ2fDA0QprTUJLdmooJCC9q/8wi/vPHloFrBlAcn8OKnP+ONv8xj86p9REa5uPqOsdzy4+AFckoKK/B7Qz9wfsNk585j+Bw6vfu0o3uPZIrDjA6CwPKc3bq2Jb5NFEVFFdXlcLrs3HPfeKKiXPTp244+fU/PaJbjDMPk/fkbQtJne30GM+asbFRQaIrvtu6ntNJTZwZT05JEueyM6JTC0cJSisrdVHhDOzsl4PaaHCws4m+LlrEiPYPHJ15Aj6SEsE1f3+09wJHi0CGbgWtauH1+3H5/nR3VZXVkUG2MMp83aJ2F4ywpeXXzau4bOAK7Vn//QqTu4PmRUyn3eyj1e9hZnMX/2/gJPtPAQuLQbMTYXTzUZ3zIsaOSunJvz7H8Z2/4PFaD4gOzrN9PX1vdzGTXTDrHF1Gz4hfI+yqpNN1c2W4C7V3JvHEwdBlPIaB/bONHnSmNp4JCC/rZNS+GPO3PnbGE3oM68vQb9U/TGDq2F/PeW4mnxrBRqQm8idF8vWQXYulupJQMHd6VPn3bsXHDobDneX/2Kl5/8z4+nbue1av2Ed8miik3jWbEyMaN9T8ZpRXeOtNjH8sNfxOtaV9WPp+t2QFIxg/owaieHevtjziQVRA2nUVNfsPkjZ9M5Za/z+JYYfgyCECrimMew2TR/nRWHcqkbXQU/556DX2TT9SkXl6exn9Xrw+7tgIE1kuIdTnJKCrGadPx1urT0DXBxJ4nHxwv6NCFlzevDpsjyWeZfJ95gCu69m7UuaLtLqLtLtpHxjMz6l7e2Z/G0coizkvqzvTuo2njjAp73CP9JpHojOav2xdWz3oWBPIm/bL/pQCU+E+srR0fEf7hBQLBYUHWUto4wq/61ju6G12iwk9+U06NCgot5KPXvq9zcfVX//ApF00eGva144aO68mAkV3Zujodf9WsWzMxGmnTgjqON288xJVXDa0zKKQfyCMmJoI7776QO+++8OTeTBPFxbhwOvSQtRYAetSTEttvmDz0+ies239iduwHK7ZwUf/u/P3uq6v7FqSU7MsqoLTSQ/9OyXRPTSTSYa8zMAhgVM+OZOYVczCnsN4+Blmrm8RjGBwuLuGOmR+z/KcP4LLrFFW6mZG2LuRGf5xL1/n1pROY9u6HbM3Kwao138Cp24hxOnnkwjEnritlk4ZdDk9uT9fYePYVh6aN8Jkm6SWhw2XnHd7Cy7sXk+MupXNUAo8NuJQJqYHsrgfL8inwltMvrh3P1ZH6Ipxp3UfTNTqR1/Yu43BFET1jE+nXxsnSgsUcqEwlynWE3km5WDKQl6q+7iETi3IjfOBwaqc2LFupmwoKLWTXhow6XystqmDO64tZ9c024tpEce1dFzL8wuCnOk3TePQvN3PvhMAkcSkE0qmHpH3weg3S0vYREWnHXRl6U4yJrTvHfk0H03PZsf0oCYlRjD6vB3pVWg0pJWvS9jPv8014PD4mXTKAy64YHHb47HE2TeOBqWN59YPlQU1ITofOQ7eEdmQf96c53wcFBAiMEFq2M52/fbaEsX260DUlgUff/IKjhSXYNA3TtPjRZeeFHV0EgRXRXA6dJ6+fQEmlJ9A5XvcaMxjhH4oxLJPv9x1gcv8+bM3KrnPUUKTdzqtTr2VG2lo2Hs0KeT3KYeeB80cxbdhg2kRG8Nn+nfxt/XKOlpeSGhXNY8Mv4OYaQ1LrIoTgZ8PO5/FlX+Ot1Yzk0nX6tAkOvp9kbOSZrfOr+wDSy/N5bP0cnh5yDe8dWM2B8nx0oWFIk0f6TOSeXuOqjy30VrCj+BhJrmj6xqaGBK/zk3twfnIP0vI38dL+t0krtDBrJBXUBGhCImWgL6e+2FdXYHRbnrDblVOngkILGXheD1Z/vzPsa1LCzBcX4vMEPqCb0/Zz208v5eaHgvsVVny9FWETgaHk9XyQfD6DqTefx0fvrw6qRTiddm6+ZUzdBxLocH7uz1+wasVeEAKbJnC67Pzzpdvp2CmR11/9ni+/2ITH40cK2HQwi39/vIJrrxzKjZcOJTkxfB7/m64YTnSUk//NXU1eUTndOybxyPSLGNI3/Fhzj8/gy/W7wr7mNy1mL9/MnFVb8ZtWyFKSr3y9Kuy49+gIJzedP4jbLhxGSnw0Xr9RZ+1NApYDZB2fEL9pUVBRydGSUn47/7uQzuXjfKbJQ3M+D5kBfVyFz8+tQwfRJjKCLw7s4lfLF+KuGkGUXVHO79O+A6gzMKQdy+RPaxazpyifNg4XUboDU3qrO7Ltmka7qBgmdAw0DxqWyb93L+GNfctDfkMe0+APm+dhyuDZ0//es4RecSmMa9uDf+36jncPrMGh2TClRaeoBGacfzttXcH/7z7Lzyv73613FFFDFaGOrlSOeUInVTo0O+MSR4Q5QjkdWj/d5jmsoszDjg0HKS2u5KLJoSNVajoeEAC8bj+zXvyGspLgqnN5mbu66UhYEsLMbwCIjnJy0y3ncdXVQ3E4dCIjHTgcOtdeP5ypt9Q/YmjB/C2krdyH12vg9fiprPRRXFTBH347l+ysYj7/bAMejx9LA3eyA3eEoMjnY+a89dz6+Fvs2B94Gi4oruCNOSv55V8/4bUPV5BbWMaVFw5gzr/uY8k7j/K/P09neP9OdZaj1O1p8KZxfE5B7ZubaUnCZZx2e/08cuVYUuIDE8Wcdp1f3Tixem2DmkScwB9HncFX0wQjO3Xg4bnzyCsPTch3nGFZdQaE43bmBCZ2Pb9+eXVAqC6zYfDX9ctYm32E7Irg0Tbrso9wx4I57CjIxbAs8jyVFHrddI2NJ1K347LpdIiOpXd8EkuPHkJKyZ+2fMW7B9LqnA/gtYyQdBpu08+ru5fwzbGdzEpfi88yKDe8uE0/B8py+fna0I7gPWXppzTrWMdGrq+A2guOOoSdjhHtmJh8/kmfW6mfqik0A8uy+O1db7Bp5b7qbb2HdKJL7xQy9gY/+dQ1HFJ36OzZnElicixHDubRtU87hl/Qm7kzllQPN7UVlGMmV6Wm1gTHG2mzs0t4/rkv+cMfp3D3fePJzyslOTmOiMiG22GP1wJqkhKOHink24XbsNk0/Jj4Y/RAe3vVB9+0LNweiz/9ZwHP/fJaHvj9+/h8Bj7DZP32TD5euInXnr6Vnp0blw45MSaSSKedksqTH5ETSobUIGIinGE7h2WZDKz/Fy4Jn11nfI9uxLicpOcXhl3+symO3zyPlpeGfT3fXcm9C+fis0wu6dyDf06YjNOm86fVi8Out7C/uJD7B45k1u4tZJQWc6i0mCVHDjKhU1dWV27HV8ewUSCk1nXclqIj/HbTZyET1Ewp2VWSTZa7hHYRJzqF7UKv40zhXZZyAZqwkVl5lG6RnfkmZ1lILUNDMDJhED/rdRf2WpPW3GYZaws+5UDZWqL0NoxOvIFu0WoOw8lQQaEZ/O0Xs4MCAsDeLYfpN6wLSalxVJR5kJaFJSG2TRT5WcUh57BMi7efn8+Rg3nYbBqGYZHaKQGbfqJyp/lNRFYxVpQTabeB10AmROH3m6xO209hQTkJidFERTU+L73fF/6GYRgW789K43iaHcOlha3/H8st4W9vfkdFpbf6luA3TPyGyfP/+57Xn761UeWwaRpPXj+B38w+PatwaUIwvFsHHLUyir44b0X4AyxwSRteYSIJjA6y22z0TEpk2vDB3DCoP/vzCxucgdxguYAh7VMB6BQTx6HS0L8FgDJ/YNTZ95npPLt2KU+ffzG7C/PqPO/bOzYGPfFXGn6WHNtPVJu6y6sBNs0WdgQTQGUdM5Y1BGV+T1BQ6BXTDafmxF3HQjq1DYzrw7ikQJPQtpI9LM5bhb9WrclCUuQrCRsQ3jzwMJVmCab0gxcOV25nfPJdjEq8vlHXV05QQaEZLPtqS9jtuzZl8MXuZ9mSdoCC7BIiol28+NRHIftpmkDTBAf3ZGGZJ562MveFtq8KS2Ir8wRGd0c6qrvz7Had3NxSEhLD59Spy6RLBjDz3ZX4wgQHn8+ofqoVMvxzoESybe+xsK9t23uUbXuPEeGy06NTUvW5fH6D71fvZc3WQ6QkxnDdpMG0T47jmlH9WbTtAN/XWnKzsWxCYEpJhMOOy67z9K2XhuyTXxa+6UcAN/btR4XDpMTj4cp+vZncv09QLqGeSQk4dRuV/obTg+tCYISpUQghOFhQxKD2qTw58kJ+ufTrkFnJNXlMg/d3b+UPYyYRYbfj9Ya/gYfLqOr2Wehh5jLUKA1SSjRESLNNvQR0j649ikxydbtJfFCV10gTGn5Z9/sq8Z1oGovRozDDlF9AyBBVv+XnvwdeY12RHY0E2jrLaWOvwI+XJbnvMKTN5Ti0+mdzK8FUUGgGsp4xjn6fycjxfTENk+lj/khleeiTVFK7ePKzi4MCQr3XE4CmYcafyNFj+E06dkoI2dfj8TPnwzV8/+12NE1w5eShXH/jyOrRQzdMHcXSJbtJP5ATvllL14iOcSH9fiptwUNHdJvGqIFd2LrnKIY7zGQwCT9/9mMsCUnxUTz/xPUkJ8bwwO/fJyu3BLc3sJ7ER19v5NlfXseYIV359ZSJLNmRjlnPspR1saTEbtO4/5LRTL9oKJHO0OazPh3asuHA0bDHTx87jF7tww+ZtaRkdcZhrh7Qlw83bcOSFkY9/+8XdO/K6ozDIf0LppTcPnMOb067kcnd+5LvruTfW9aQW1le523ZYxpMnPMmbqNpa1XowkbfqPake3JCltWEwJO4JWV9YxjC6h6dhF5jYpxhmfzfzhc5UJ6JgYlAIJAMju3L1tLdIcfb0XHZTqRp6RLZgWRnIkfdOUGptTUhTdcnKAAAIABJREFU8Jpp/C99C6MSrqdf7Hh+v/3v5Ht34dQkZYaLQ5WJlNpddI0qwCZs5HjS6RQZbhl4pS4qKLSwfduPMGRMT7auSa+zqSa5YxtyjzYuDXOvQZ1Izy/Fq2scbwB3uezcMGUk0dHBw09N0+KXP3uPQwfzq2sCb7+5lA3r0nn2+VsRQhAR4eDfr9/N9Jv/TUGYPEl+v8m11w3n/7N33nFSVXcb/57bpm0vsCywVOlNQBCx0uyKokbs5Y0mmqiJqZrYEhM1iUYTu7HFFiyxNywUFVSqCAjCsuzCLtvr9Hvvef+Y7XNndhYwmvfl8cPn49xyzrl3Z87vnF95noOnDuHe5z9i845KVFUgiU30h04cjK6prFhXTNQhEB5ojVfsrmzgR79/ntPnTGTXnvr2GgbTtDGx+fWdr3DYjOEs/no7Lk0lGImtXQWQ7nXRlEKsQRKbvEuq6hwNAsCxE0c4GoVMrzvOIERME4SgIRjknH8uotYfiE2iAvqmpdESjtAYih+XW9eYPKAfq8qcjU/QNLn0uZcYNTKftbUVGIqK0rrLSYQdTal8PySay0I1TKQUKBEXd0w7nbcrvuCp4k9piAQd74pN4anvFNL0rt+z5TWfs72ltJ3ATiIxpcVXzdvRhU60G22FoihMy+1IxBBC8JsxV/KHzfdTEarCtE0kFoWuOgQtVIbgnYq/s7LmLXzqV3g9MUMmhGR3MJuqSDoFViOasPCqzsVvB5AYB4zCfkAoFOKlv7xBZn4GJ142F0VVsBNU8GZmxRLfQ0kmtU2rSlLqV9NVZp82hZ8dM4pHHlrChvWlZGR6mX/6FGpqmjn3e/eiaQonnDiJBWdN47NPt1NWWtvFNRQOm3y5YRebN+5mzLhYeqimqbGgdHwdFADPPbOCMeMGcNevF/Due1+yu7Ke0sZmPlm3gwee+ygWPCdGZmfoKqFwNI5yQhLLUHrs3ysxHd5VKGLy/tKvUASEsgTSFSOszstoKxxIzVdt2ZI1xbHJuDkYZn1JBekeg/FF/VAUwaurnNOEA+EIwUgUj6FTWt/AdW8sZnXZbhCCdJdBUyjcJcBc3RJAOrg8IGbIpgwcQPSjTxOOM2RarCkpJ+K1CCdxH6UKQ1HQ0gMougWtyQy61+aHnz5FfcRPH3c6GbqHUn9d3L2aUECQEmsqwLq6Mia99jtGpPfhF+OO5eOaVe0GoTOi0sQtDEwEuqKhoMSkN0d+nzTN2+XaPFcOd066nus3/Jnq0AYGeGrRFbtTW2FqIptQY1RJ7Sj0NNBiuWgxvQzxFZDrOkCv3VscMAr7iJtO/xMfv/xZ++e//uAhRhw5nuLS+EwSIQS/ufhhph41ivmXHIHpUOELJDQo3WFGLT58ZTXHLzyUW249A4it5C+/9B9UlNcTbW3/yceXs35dKUOG5hMMxrsNTMtm48bdDD+oAN1Q2bOnkco9ieknwmGThx/4gN276hCKQliTNHlpdSV1PFNeto+fXjSLN5Zu5OM1xY5tORmENggACe4GSaBPrGCvJRTBTOoXj0dhTgbPLl/Hna8tR1cVbCnJ8Lh58AenU1HvTKqmKgoN/iA2kjOfeI7GYChmBKSkIRhfOBVJMCYBDMzKZOrAQn44czr3frTSMWNISoll7lsWU2coLhOvWyHUOrELEYszlAViRqClJYwuVHShtlNSQCxoPDg9j2vHzOGW9W9QHmxo3zkkS2MF2NhYweUrn+bYIueKP4kkKGPGPGJHcSkGPz7oQkZnDGNx5UdUBKvYE6phc9M2otJkfOZItjXvZIivuYtBSAaBJFdvIc9VwBlFN6R0zwF0xQGjsA94+/EPuxiENmxdtoH+h0+kclfXTBIpJbWVTbz34ipWvLexXS8hGjGxU+Vz7oaSLXt4Z9GnnHx+rOL0o2VfUV3V2G4QIDaJr18fMwoulxanp4CUPPHoMh5+4ANyctKYd9x4DEPr0kZ3bN/WEXMI5ugg4kte/MEIhfmZzJ0xijUby/ZJr1mJxorJ2jJvneA1dCzb7kI34dY1Zo8fzl2vLSccNWkbQjAc5bIHXmTsgD4s31wSN+Hpmkpeho9/b9hEKGruU9rpQ2fNRwjBFYdPJ9Pt5neLP4xrTwqJNJz7MBQV07Z6EK7sgIpgdN9MvvbvSXpdVFoIwFA09Na0Mp/mIkN3c8Wnz6AKheMKx/GrCcdxzafPsbZ+V9L2ADxGA1ta4iu3nRC2I9y99XF0RcOSVtzu4vO6L5BIwrbWY+VzGxQBhqrws1F/ictSOoDUcKB4bR9w31WPJjyXb1jc8dwPOe7s6XE6A5ZlE2wJEQqE+fOiKzn+7EMZOKxPwraEEPjS3eiueBseDkX54OU17Z83fFHmuBuQtiQzy+uoeWCadqwgzZbU1DTzwqJPu2gxOKHLnJaowEsIQhGTWYeOoKhfdsrU18kQtWwmDe6H1u053LrGVSfNZO6kgzA0Fbeukel1c/0Zs9hYVkm4W4BXAtWNftbsKI8rsnIbGledOBNdVdlRW0cwheyiRFCE6GJw5oweRt8cX5d35tJUNF3BdnU1CgKYNXAovzjkCHxGYn3k7rCQrNvZTMSvJ1TUa4MtYV7BaG6ceDJ3TFlA1DJZU1uKJSUR2+K9is3M/+A+vqwv77HfdFeIfJ8fUjZfEJVRAlbQ0d3UtjepCmdgO37J4o9ZUuBTw5QHnavh2xCxQ1QEt9IcrUl5rP9fcMAo7AOCLYn5V0q+LGX8tGEcecJE3A5FY9GIxRcrixk+bgA/+v0Crrr1jITiMZqucMXNpycUsQkGI+0FZwX9sh3VzxQFdpfVcfa5h9GvXyYul4auq45Vp5GIhaYpGEZqUo5q0HJcvluWzZhhBeiaygM3n83l35vpcHcHZh48BCMBh5LduugryEpjcJ9ssrxuXLpGmtvA0FROnjqas2dO4g/nHs/S3/2Al391IR/ecjmnThtLvT/oODnaUtISirRn3KS5XYzsn88fzz2e782MBT6LsrPR9kUDWUCaEfv7N4ZDnPDyE5SpjUR9FrYqQYOifln89fQTcXWTzpTA1L6F1AYDNEd6V8QnJUQCOhF/8tWyEPB2+UaO7z+WikAjIdvsko4alRb1kQDRFCb6XI8/KcHd3iJiuyj25xO1FSwpsKWgwD2KI/PPJ5ZGG7tOSlCQ6EqY9ysfTtjeypoXuHvLQp7ZeR33b7uUf+38LWErMWPr/zcccB/tCxKVfwJ+zcvT97zLoXPGJnTD1FY18ezf32PgsD5Mnz2mdYKObzAasbj714vwpru7UGdDLB21uLqJ+Sf+het+eyrzjhvHU08sJ9Jt4RUKmbz7zgY0XUXXVG78/QLyctO44rLHMR3I40Ihk5wcH6qmUFfbgpUkPVYP2Fheia3RhfZyYL9s9FYiPbehEwpHE1ZwK0Jw4lHjCIajbNy2B9OysKXElrFAc5vvoLSmkdKaRjRFoCgKZx8+ibNmTqAgq4N7x+c28Lk7DPHsCcNZvX0XwQTZXhB76/1zM3j+Z+e1H6tu8XPX0o/jBHF6g2yPB1drwdyzX31BcziMhQQv2N7Ye98arWFUbh75Xh+7ulU137EqQXFdShBEgzpZGQJTWrGiNIdJ28SmPhLgq6bKhJKaqUBT9/49JYIhdKbnTGJV/QbWN3nwKTY2KmOzjuOwvDksq36y3a3UeX1TFSpxbG9L0ycsr3oKk0j7T21n4Ate2/3nAzGIVhzYKfQSj173DL867haqd1WTU5gdf4EQCJ8Xabh4+p7F/Plnz9F/iHOu+57SWp68823u/MW/+P6cO8jKdyaTA4iETdweg/QsL26vgRQxg2B7DGyPgWna/O6mf2NGLW6+9QzHHUA0ahEMRGhqCvK3u95h0OB8PJ7EK8n6ej/zjpvAlKnJef4F4KqNxnYMndDZXfT1ziqeePmzhO4MRRXc8Lc3WLNpF+GISUaaBzNdJZAvsF3xz2Lakohp8ezydeSkJS9OOmnKaAb3ycHdg/tqy+5qpv78Hk6//UmWb9rBrYuXOAaVe4P6QICfvfomUcvisz27CCfQO7jgnRfiDML+gEBw5YhZ3DjhFEc+qDbMefevNEYCeNS998MHo/vfh68qKrtDlUTsCCDw2ypBGxaVvcnahs24FefiTK+WEXfMlhZvlN8ZMwidYMkoxf7VBMz9//7/G3HAKKSIf93xMnOVM3n2tn+z+t0NnFN0BY3Vnb5EQoCuI7KzUfJiRkDakpKvKshOMtlDTGu5qryezBwfhjvxD2tPWR1PLL+O8XPGYmd5MftmYuemtS+RpITHHllKWWltj66f+jo/u8rquPh/jkpIey0lPP/cSiZOKsLlEM9QVcEh04cifRqRbA3L29GOy9CYM6NDf/m9FVuIJqCzVoSI1Sd0ykSqbwygNPWcZSSRrNvRNbC5cstOrvrHK1x4z7/455LVWLbkyau+xy9OPYpDRxQlbS9iWmzbU8u1j7/O4i17V0ndGZaEd7dsZ9pdD9BUF0q4s0xEb7GvkMC9m5fxZWMZY72DExrliG2yuFWGc29R7U/Dlr27X0FBEypuxYXS+l/b8eFpg7h2xKXsClbQPcwetiO8Wv4e03MXoIuu8RYNg/7u0SwqvZF3Ku6lJlwKwHt7HiZsO7uJpLT5unllwrTi/0844D5KEY/86um4Y1bEwpPhJtgUQmSko2RlOa7Qt6wrRVWVhOpjEDMgxZvKGTt1CBtX7Uh4XXNjEOkxsNOcdRG2b6/Cl+aOzzDqBiFiPv9T5k8h4A/zyENLHK+LRCzeeH0dqqqgKKI9S8pWIZKusrq6mn7j+lBaUQ+2xLIlbpdOUb9sTp3VQfds29KRplrTYrxOjmOUoIXA9DqeBmLvTe8Ua3nkvc94aPGnhFpdRZvKKnnp040885OFnHHYBMYM7MvZdz6T9N0AhKImZoT99gvxRyJ8WVKF4hPY3v2XetozJE2hCC/uXEuOy9tjBk/IiuBR9b1yI0UsjZL6bArTQmS4o0lps9tgY6MJnfn951EfaWRM5nCmZU9EInGpBttbSlGFipPoRWO0mRl5PyZsB1hV92osdVbaqIpOsX81pgwjUFjf8A55xiAqw85p0bFxWLxbcT9bmj/mtAG/pj5SgVfNJE2PZwX4v44DRiEFXH/yHxOeCzaF+N4v57Ny2VZ2Vzjz6PibQigJgsTdsW1j4rQ/IaBiZy2Tpw5mxSdfO16z7etKirdXdZnAneD2GOS17mC+d84MHn90WcLJuXx3PS6XRmaWl/o6P7YKwXwDBERDEZrLajF0lSnjBpLudXHYwUOZM2Mkht7x9Zp96EheeGdt+2TdhmSGEkC3BMnMm6oqTBgc05Ru8Ad58J2VXVJSw6bFjso6nv1oHecdeTCXP/BS0v46QwtANN4LsdewpUQLKES8vauz6AmGojIwLYPtCaqchWITlTaVoZ6F7iVwxqDJLCpZ3V5/0BtELI0qvxu3kXrg1pY2z5e9gRCCdyuXk2Nk8ctRlzMsrYiB3n7OiwmhMSV7HEIoHNP3Yg7PX0iLWc/aujdZVf9qjBgPkNhY0qYyvL3HcZiEKW5Zw11bzkLKWGVGH9cQzh50q6M76v8qDriPUkDxFyVJz7/8t7c4eHpyjWM9xUyesEM6aRukjK2sTz51Mu4kbibblgkNgq6rKIqgscHP6SffxWkn38k7b33ByNGFyccVNmmojxm9SLpGK69A+/lI1GLd5l2cc9JUTjhybLtBkFKy5POvufufHxLtVuClaUpSdmVdUzl/TnIxlYhpccLvH+WfS9ewrrjcMUPLlpJ73/yEWxa9RziBGE77tQpYeixek2ukplLXK9j0hlE6JURsi6dPOAshujcsQYBqpO4SUVEoDzTulUHogJ3QTeXknjKlhYWNKS0kktpIPbduupn7vr6Mu7ecyZSsKvoYofY7daGRoadxSuGc9jZ0xU220Y9tLZ+2G4S9gcTCkiY2FpKYMflH8RV73d5/I0Qi5an/6CCEGAjcBcwlNt28B1wjpSzt6d6pU6fKVatWfaPju+mMP/HxS/FFap3h8hiY+X0RCdIX0zI8tDQ5c830CiLGgDp97li+qmpiT2Vjj7noaekuJk4aRF5eOh+8v5HmpvjgaX6fdKqrel5JAgT6Gkg1sR9i+MA8Hv3Deeiayt+eXspLi9cTSlD30D0bSQqIesH0CIQiGDEgn63l1Sllvk8Z2p+1O3Yn1VxOBElsV2AbrR8EHDpgAF9U7SHQgyHpXT+SaJaF1GP1Cd2lM52gKQoTcgtYU524VsDnUojqASItBm0pRkKReDKDKL3wB6h0rknfOwhs8nx+cr3x33ejlfso2Z8oXQsy3FeF2snIqcLAlqNpMLM5OGssJ/Q7mnQ9vnL6ke1XUB0u2ccniMdZA29hWPrU/d7utwkhxGopZdxDfes7BSGEF/gAGAVcCJwPHAR8KIRIoJD7zaGmpobj3QuZq5zJXOVMrjn8N0w8alyP94WDETJ8iX99/iQ1Dd0hkiV7y5jmwaeLN9IXeGbRj3psb9y4gVzx47msXrXD0SAAKRmEtjoK0QN767ayGm742xtU1TWz6O01CQ0CdNXolUAwRxD1CaQmsBX4KkWDALC6eO8MAkA0rbUWQhD7VQhYX7mHqQP7712DCSAQGA0anjqds0eM7zGsK4hVKNeHg3jUJN+vsEWkxQUIVMPCkxXEm9M7g+BSNEZmFqR+QwJIFBpCzhlhbtXFUN8gNKHiVT2tYjxd0d9d38UgAFgyglvdwR/H/4zvFZ3oaBAglmG0v+AVYUYZ5Uxx76C0/jqawxv2W9vfZXzrRgH4PjAUmC+lfFlK+QpwCjAIuPw/OZDSr0pZ2OeHmJ383hs/2cJ9VyeuXO6M+s07UBKsoDvTaSuakvC62MU9z2zRiMm2L3fRXNuCmqwtYO2aEq69+ml270qNeTURbDvGCGo0mYm5JlqxbNU2Vm8sw0oQp+gMn9vA69ax3CA1wTdSAZUEErDdxP0agqbJlupa8n1JIt296kdiaxLpkhw2pIipBQPwasnTOCUQti12NNUjRLLcINH+z4qomCEtJVoIiOlOHF84jkcOu4DyQGLOKwCfZnDBsENRk5oziSqc/+5NZgu7ghX87eCb+Of0vzA5O37B5VGdFxFhK+CYPRQwG3mn4j7u2XIutZGypOMHUIXO5cMe4bxBf8JQvBiKB61bBlOaEmSiu5QctQWPEkW1i/liz0Lqg5/02P5/O74LRuEUYKWUsj3/T0q5A/gYOPU/OZD/GXttwnOzzzs8pTYu+81JPWrTZub4GDYm8Qq0s00wXIljEYoi2FVcRUG/rKT9hcMmjQ3+hCL1nWE4pJ52H5tuQlqLM/FAG2xbUl5Vn5L73Oc1+PVl8xgxqiAhZca3hZZwmBcuPocJ/QpwaSpube9yM6QiieZYRLMszAyb5S0lrKveg0fTe/Uj/O30o5kzMHndCAiioZ4pLtpgScmgtBwm5xbRFE3s4nQrGoai8WHFFmYVjCJ+5JIcj58RuTUMzkpsXCxpsbgyVpR33qD5cbuFsO38jjXFiBPMidohHt9xNevq38ZvpbboEQj+UXwlteEyxmYcgyYMFBTcoqPmYahejSpk+/pEALYMsb32ppT6+G/Gd8EojAW+dDi+ERjznxxIsh/Rh899wjNlDzB5zvjEFwFLn16KK0lBGEB9VTM7vkqBNEyAZiRuKxyKEgVOP2Naj01FIqltq6+/cT6jRhciBAlpN2xbku/z8rffnJG0rdeXbkppjm/2h5kzYxTHTD4ojtPom4AAfC4dr0vH5zJwaSrZ7vigsgCG5+Vy8bMvsrmyKraiHnVQr/vKcLties8aoIAtYrxCT25cw6T8frh72C20IWBG+byynIn5/VLrvBeutCe2r2DCq7ckvSVkm9RHApQF6lm8ZzOKopBvdLhxMt1B8nwBFEXG8okTwJQWVaEYL3uhpw93H3wDOXqH7kF5MBurW72DLlzMyD0TRXRdJG1sXELAbMJOkqOmoNF5tWHKCKYM89aee1jb8CYBq5GIDBKSHfohaYqzmzVoFlPeGJ+e/n8J33qgWQgRAe6UUv6q2/HfA7+SUsYtG4QQlwGXARQVFU3ZuXPnfhnLXOXMpOcX28/3eJ3hc+MaUkTQQVFtf0NqCvrQfHRDw4xaBALxpGJ7g7Q0N/c+eCHhsMUVlz+WkOJ71jlTeG2Jkz3vgKqKpBQZbZg0uj8uj86S8tJe0KntHQqz03nqmrP56+sf8/baLZiWRVqmhwZ3BMu2sVoV2xRESoHgZBiUncmMYQN5uuwLR4nM3sKlqjFeo560DoSNLzeYsgtpb3FY/lD+MPk0Xty5hg9rX8ZMQefCpRhcMuRM5vTt4MJqjDRz2errMVslO3P0FgZ46tGFha54ODx/IYfmLmBXcBNf1C/GlGGKfBPZ2bKOzc3LHPvRRIzqJEPrQ0N0T1LD0R3T3NsxlER06G7GFzxKprvnxdh3Gd/ZQPPeQEr5kJRyqpRyan5+6qL0bbjvp4+2B5JvXHB7+3ElGfGZCh+91pqBlOSyiD+UcBLdn5AAlk2wMUhTYxDLthk5KsUVZA9oaQnxw8seZeiwPqQnKJKTAt5atjHhbqINliV7vAZg3ebdfLqmBKPWjgWy7Zh2Qco+kB6gKoJR/ftw/YJZvHrdRTy5ZA1vrfmKiGlhS2hqCGLUSWYWFXFw/36cNXG8o05wb7GrsYln125IqhvRG4Qti4ht9bADk7jSIt+4QQD4tKaEfHc6Pxh5FIieJ11NaGQbmRyRd0iX45lGOucPmo+h6AgEddE0trYMQ1VO5qcjFzEj7wyWVz/Nv3b+hi8a32VT01Lerrin1SDEP6gu3MzIPZsJmfNoiFb0yiCAZJcZv1vpOBtiV2Nqccb/RnwXitfqAQcSIXJaz+1XdF/lf/LvVcxVzmSx/TxPlPyN84uudL7RgptP/RMA6Xk+mmucC9UAzIjZY/EYkJRQrye0CdBoVU2Y/bKIRkxqa1voV5hJRXnyYKGuq5x19qHMnjuWSy54yPGagD/KmjU7aG6O9zFLwHQrSTWJO8O2JS5DJZyCC0uNgqdaItWYPTB9YPr2bXZTheCZaxYyuG8OHkMnHDV5csmaOE2DiGmzoqQ0lvVUWb1f7FF3xbneQBGitYgqHm3SpO3nRCzGI1SJ4Y2guZyNkI6SEuNpqjAUFb8Zxqe5GODpR0nAufhSAFl6BkflT+f0AcfiUuOZg08qnMXYzIP4oGoFISvMjNzJTMoajSIUqkMlfFzzLM4/mPhjpozwUc3TyL1MsC03M0lTQuSrLY7GNWJVdvncEt5EY/gzdCWXXO8cVCU5H9d3Gd8Fo7CRWFyhO8YAzlqJe4mfzkrMgnh63kW8VPM48686npfveStpO8kMAq3beyFiBWvRJBOhaP3Ray4NswdaimQGRPGHsTM81FQ3p7Qqj0YtTltwCFnZybNqbvntS+i66lzp3DpjCtEWhEveZzhiUdgnnfIUUl8FIFpfm4zEDMO+QFUF5939HLaUDCvIZcH08Y6iOcKGaFQijVjmUSIoxP4UqU73AoFeqxLNsWKFACnClhIliV5y2/cHJN7sEIqaQtbafnbOha0oM968nUG+HC4YfiS7g4uIyvh3J4FBvgGcP/i02Gcp2dS0jfUNm0nTfRyRN5VsI5MhvoFcOmQgEKt0lsR4iRaV3khPb1xBxcYipr+2L88pMIRCnmY6dikwyPEc1focNluqf0Jt8H2klAihsb3uJsb3fZI0l9O09t3Hd8EovAr8WQgxVEpZDCCEGAzMBH6V5L5eY8OSxMIbzXWxif7Kv17C9OMn8/gNz7Hl855L47tD9I2J5di2RNiSzFwfjbXORqQtTVXXVKRlJ03fTMtw428O0d2jISTQyTXRtjvRdTWpctqushqyspOTw7W0hPE6aEEI6ELCd8z0EUSjFsvXJH9f5VXNqe2gWiEBuR8cnJFO73VreQ1/fnVpwmvVCJjxjxw/rhT7lkgEAkUKlIDATpO9yrCykRiK6hhDaDNsiiodqpm/eQha5XSkTXFLDbdvWMrPx5/MC7tf7qLJ0IbqcC1r6zfx1M5/UxqoaJX4lOhC49nSV/n5yMuYnD2W5qifh4qf49O6ddjSZlxGNh4lOWGgS/EyPnMO6xrewZT7Hs9TRDrDs39GWeODRO3qTs+soavZFGZcBECV/xVqgx9gy1hgWsowtoRNVT/kkAFLe8xE/C7iuxBTeBgoAV4RQpwqhDgFeAUoAx78NgY09dhJ/P3T23q8rosCpcuFUjQQ1eiYUSzTxpfu6XESCAXDeNOTUyq0NIbiqI8lYKa7sDPjt6rJDAJAZusuoeeYgIOesADT0/HwH3y6ldWbSjl8cnKqD6B3sqNi71xHfbPSUJL8GG1bJjzvZIQ6X5nn9WL0Ii21c2WBFlQQUXrtMuyflo5X09G+YxNM98cIWhEe3LoU4SDNqqLQx5XLHVsepCSwGxu7fQcUlSYRO8qdW/9BxIpww8a7+KxuHVYr7UV1uASzB+oKU0YJ24H9YhAECkPTplKYcSHprgkIOmoYJJJczzx0NZYGvqf5OWwZ72KN2g0Eolv2eSzfBr71nYKU0i+EmEWM5uKfxH6D7xOjuWhJenMvobs1oqHEboGeso86QyiCfkP6ULengYjhRclIdxSRLS+pYdTkQXy1JnGGlLRJSI/RpU+6+pLNvHRwaQmLvRIJ2gBcfN5DDCzKJTvbS22CnQx0ncRl6yCiXgXb1XW8gVCUj9YkZnftDENXycvyUV7tzF/f1mM4S2BrsWebNnwgIPlsW886wX0y0qhsSPzVsWRio2A5qF72y0gn1+fl0ulTyPJ4uOS51En1OkMg0BoUrAw7ViyXInY1NyIRDEzPYmdzQ5zry7YEUgpEt2m6p5BVWwGaEIIZ+UP5qGrbPtIyCZoiNrkytjtqm/QVBIaqs6lpW1LmVCklb+1ZRlWoFrNTZXLIVpFJn0YyMej8AAAgAElEQVQwNfsUvFommjAw5d5n4alCx6V4OabvxTSEPqIhtALZJaPKotK/iP6ZF+PRi5AOrrLYiETCc991fBd2CkgpS6WUC6SUGVLKdCnlfCllyf7u583As/utrUNOmkh6ThoX3noWRxw3FgQJi8OaalscJTm7X5PKQrDtEunRwZ3YIEAPdBlAWWktoSRGEjp2HJYKUZ9CMFcnmqGlpqKeAJGoRU2Dn3NPTswlE84Eq5O4zq7aRiYOSU7a14YNpckF6xUB00cMxNBUNFXBrWsIpZUR1cHnX97UzIaKSq55+U0ufvZFx3hEd3iS7CbakloKvc4CMd0RlRJT2pQ1J3KhCMItRnuylpSxlXmeK3H7aZqLw/sOByEwFI3VtaX7pKUQg8SjR7GxUYXKAE8BuUY2B2eNwbJtRx3mzgjbEUr8ZVjdqCoao15MqSRc4KSpORzd52ImZM2Nq2PoHQRF3glcNvwhMvR86gIfYksntldBQ+hjpDTxGWMRxNeZKMKFzxi9D2P59vCdMAr/SbTVGqSKASOc0zw/e3UdWz7fzoPX/JOPXliBoigJ/YeVuxs4+fzDGD4+OY+OlDBoRN+UfM5Wtq/HibmgIDOhrnMb/P4wefk9T06KBVGfitTFPhmENui6yvQJg8nN6hpFlsRYSi1P13GX1zcxON8pSa33sCWs2FJK1LQwLRtbSiYPHRAjw+sBqa6kbSnRu+3+bE0SzbORrf2UB1rQHFwtiWC2akm74jiQJLrbjAX9WwP/Ps2gNpxst2SztPJrLGkTsCIErAgSmdJ43FqUQVl1jMyr4qDcavK8LYCNIiTZnpgrRUFwQr+jeWjqrTSbASIpMJdKJMtqPo8LVEsExf4iTOlc5Be2/ewObcarZbJw0B/xqckr/JOhn+cgPGqMUl5VMhCOzhQVyw7z2a6jqfa/guyS7mqgCA+j8u9G7JOB+vbw/84oQMwwdP6XDJFQpP26x0rvcrzGilrYDYkDYZZp8fyDSyhJoYp52Jj+vLX9z/gyEvsXetoBtKGqsom0BHUGnTFn7nh68l4JwFMdhYidcu2AmmSc0ajFM6+voqkl2F7FbBgqkTQI5zjfd9O/FqfUb6poe4qIafFFSQUee//9iMOWRZbHQ57PCwJsYRPNstrJ9tpgyuR0Id3hUTVOHjqyYyciJEZaBM3VaXUtoMkMJc2/cRLRkYChaNw7fSGzC0bF3wToqklRVj0ePWaEVEWS4w0wMLOBwVn16G06zULQx5ULQLG/R7LjpNCQZOsmQ3zOY7JklN2BWBJJoWcEh+WdjSp6Lw2qCpXhaR0FaX3TTkM4EPYJoMr/byJWFZb00/ZNEmhke47kkP4foCmZbK66itW7j2dL9c8JRFNzrX4X8P/SKPQGC687rf3/Lx2SmBvJrOtZTtGM2j1SYDQ1xLarC388N+E1iiIYPaFn9k7TtB3rDLrD4zX48U+O6/E6RYK31jlNzwl//81ZLLrzYjzdtB/chkZRYQ5rNpYR7STDGYlYKIaScCcS3U8FYI5tmxZWYP8WHVb7/Sz/8fd54Hun4i3QE+4Ae+PHj0qb66cfzfrzfkyW4cadEcLw7D/fdcCKsKhkNZePPBKXA8VqricQ9xiKAJ9hYmgd709FYUJWzH3i0/aFUFCSoQcocNVQHnTOHrSx2Bn4ghU1i9gd+IoCd++oSNp7klDoGdn+2aMP4qDcP6AIN6pIi/1TMhiRdweB6DboZnYlJmGzhEB0G+sqzqAm8BaB6NdU+V9hbfmptIQ37tW4/tP41gPN3wUoqsBOQMVw0mXHtv+/3cOkVDgkl/KS2qS/8mQiOgB9+2chpeSoEyfx2O1vOKap2rYk1+tOqfgtlWyfstJaKsrryczy0NQY7HEjIMI2eFWQybu/+rYXiURM+uamU9gnk7KKetJ9bs46bjJPvLySiINms15nQ65CZP9rwCeFBOz9nNyT6/OiKgqHDOiPKez9QvZ33qhJRG2beS89Rtg2scIqqm7v1+rlpZVbqQk1c8lBM3lwy7LWFNzYX9qtmSn1NS1nImqrK+qkfrN4YddbXWIKCgp2SrUEgrqoF3+zwbj03Qn7Lm5ZxY6W1UDbd7JreZ8u3Ehk0uwkG5Ni/2qGpXXEuvqknUKudzaNoc8QQifTPY2IWUl8WD8Gyw7xZeX/IOkcP7GxZYDiuj8wod93nzfpwE4BeCe6yPH4P0r+0uVzel7yKqq/vnQ1J54zA2+6QwpLinjj6ZWcdNAvuO/Gl5h1WrzqmFQEpsfg4yVf7TcFr/fe/ZKNX+6msaFngyBVUC0ZC2iSPLwQaaUgr6xtZteeBm679lReu+9yLjh1WpwsZ2eMz8zn1wuOoSArvcexC2Dm6EG49H1f3zhlHu0tNFXh4CH92NXcyKNfriZs7ftq3qWqzB8+mp8ueZOy5kaCpkk0pGObomdDjmBcViGnDJyArvTsJtvYWMEDW5a11xK0YVdTJttrc6j2e7ESWFGXYjCvoINVeH7/uczucxi60PCobnShMSW7N4Vdgqit0mgmrxLuGKtsP6KgclDaoZw64JdcPORuMrTktDjv7f4VJfV3YnfKYFIVHzneY8j2HI4iDFzaAHQ1z2GUGmGrolu2UgeaI+uS9v1dwbdOiLev2N/Ka42NjWRmZiY8nyht9cLfn8V513WcO37oz/Z5LHkFmTQ3BAiHYrsL26Vh5aUTk1nct4CvEKBpKqZppUznEPUoRLLafNkdffeGrcPj0lkwbxLrNpfx5TbnLKF0n4t3H/kRd/x7CU8tW5tiy72DIgRWpwc3fWDtH9kEoHVlrYPIgMx0N1WBJFXwKcKlqrx40jnMf+1pzC5FKxJ3ZghNd96N6ELl1dlXUuTLoTLYxMkf/B2/uTdpm22r75i6mq7aDMmuaw1uCxQEqqJybN8juWjIgri7/WaA8mAVALlGFj9c/VvMlGkoJP3dDfRzJ6dw6Q6BwK2kU+Qbz2F5Z9PXPZS3K+5lXcObjteniRCTPVVkeQ5jbF9nChiAptAaNlRehJQWkjCKcGPLMMl+CbqSz6FFK3o1/m8SiQjxDriPuiGZQQC4+5ObufqwG7scm37SZI67ZB63/OAxAs0hzvrhLAYd1JedX1cmaAV8GW6CLeGk7p2aPY3oRqvWMWDlprWmoO69McjPT2Py1KFomkJVVROff1qc0n1SIWYQHAxRb5YVwXCURW+vYfjA+JVWG1SXij8U4ZLZh/D0srV7vSHyGBrBBDuSo8YOZXHpdqSMZTv1hn4iFQgEREHWSqpNP+zjLkQhFlM59dWnYnUWuoXLG0HRbGxLIRpUY0bBAWcPOYQiXw4AfT0ZPD7zIm5a9xpfNe1BShyrjxM9VRskClELWsIezhw0jb7uXExpMiV7PIN8zvGutfWbeGTHv4jYUSxp08edS3moKsXnl7jV3hsyiSRoN7Gl+RO2t6zirKKbmVtwGV82vhdXz6Bgk6s2YxOiIfQJgcg2vMZwx3Yz3JM5pP/7VLa8SMgsoyXyFS2R9UnH0j/jkl6P/9vAgZ3CfsBDv3+Ffz+6vMux3IIMavc4F2cBaLrKfW9dS3lxNb+74omEFBfjpg3l6w1lBC2JlZ9Gj2lCKcDr1Zk1exymZfH2m1+kdE/UoxDJTF4XsTfoWHt2fDZzFYYOzue5n57Ly59t3OusI11ViSagvtZVBauvSiC69yLv3ZFox2QLSTTP2itbrrYaYbsTMZ6qW7gzQ13ss5Qw2NWHimgtNrKdpltBMKffaC4efhh/3/Ihq2tL8WkG3xs0lQnZ/QlZJteufmGvGWEzdJ0VJ1zf43Vbmou58cu/dkk31YSKLe0UjJLEpZiMS9+Nrrj2qWo53zWI/xl2P1/UL+adPfe1t6VgYwiTSe5SNGEDChmuqQzPvRGfMTJ5o8Ca3Sfjjyam0THUAg7pvwSCT4H/cZDNYMxApP8coQ3a6+fZFyTaKRwwCvuIuupGzp3+O8dzbq9BKInGgaIKjjppEsveWJ/UKJx39Tz+fttr7KgL7LdJWQiB4dIwo2aPegeSGK3FN2EUbDoV5KkQyRBYLoHXpXPXxSczY+QgSqrqufrRV9hRmTppriIE04YPZOXXSdIh+6qErNQnawVQFYVod76RHiCRRHKsXu/LVeCcUZP419YviHTq05MdQNXi/2Y5ho+puUW8v+erLq4xvdPk2/kuTSitNQ8Cv7k3E60kxy25bcqpDPT0o8hXyMratTxX+jo1kToGevpx3qD5jEgfwmWrrqfJ3DuCgklZIzgkKwNViTI87RBeKv0DEZyKylKB4JejX0URKuXBrSytuJ2GSDHZajMFWmM3bWgFgU6e7zgiViUebQj9My7CawyLa3Vb7Y1UNP8LHCi6VZHOpMIXcQceh+DLQJuAjwIiDZH3BkLtu5fPs/f4P6Wn8F3Cg7e8mvCcooikE45tST58ZS2DRybWQYiEI5Rtr2LW3PFoxv7z9kkpCYeipKW5ewxNCEAN7Z/sme7o/AUMtxoEiNUObCmPEZEN7pPNK7+6iOMOHpFyu0LAsQePSMqBRKWFqxbUACn7wEb2yW9fvac8FgRanfM9ydrKdnk4dfhojG7FaonYUOsift6t2NzFIABEpYXlwLVqShu/GcFvhh3/tF2POdMAZnnruH/b0/xyw+1cvfYW7t76OGXBCoJWmK0tJfx+8708sP2ZvTYIbuFidp8jOa7wEuYWXM6QtMnM7Xd5N01lgSp0pmSfzJiMY5K2ZyhuROu3rtAzgtOLbmWCp5ZCvaGbQYCYYy1Mtf8VGkMr2dOyiLUV8x11mgdkXoYiulc/qmS4pnHIgPfxKOkQfIkOgxBrHxlE+h9P8W38Z3DAKOwjku0ELMvm4cW/wNdDAVnJlgrHgjRFFWz/spwHbn6ZJ+98G6WqEUUINE1pDRTv+5+vsTHINdcez5VXzWVUEpEeIUHxWykJ3/TWdrRFSVyNHW27NJWBubHK1Khpsae+mUtmH5K4kW6wbMnrqzfzk5OP6HI8bmqToPpBdVZf7AIbOHfKRG44dlavtZoVh0rho/sP4fbDj01IiRGwTN4p+ZpINxdYNKTtL+2hdnhUnZn5wzA6ZSZ17iLP09zqVom9QUXY9E9vwFBtgnaIiB1lV3BPXOVyxI7yUc0+7OQFcbQXE7LncsbA31LknUCWXsC4zFl8f9gDzOv3Qw5Kn4YhnLOUBApTsk/uwjwQtAUbQkW0WO4U3qmFLYN8XfvrOEqb2sBipOzuqrQIm7uoCywB82sQToGlKES/mWSKvcUBo7CPOCdJkdmUI0cyYGgfXvji9/zyr+cmvM4ybU4+f2aXwjahxHYSlmW3M5ValsQORnC7dMZPKKKgIHlQPFUsfmcDpy04hB9dc2zSXYMWsVPKeuqXn4nb0HpvHOzYP0UI0j0ujhgzmEff/5wjf3M/p/zxcS64+1+M7p+60l59SxCfS+eGs+aQme5uz4oXgK1COAvCeRDJS61GQRGCeSOHc9r4MRhq6pFpiSSa2XXCUIVgXF5flpWXxPQ3HO4LmFGe2ryOAekZXY5HWgwC9Z44GvV9gQSm5g1KyH9UF0qjb3ozQ7JrGZRVx/CcGtLdqcVjzLjJMnVE7BDpWqDbsSC14TJUoVHkncC03PlkG7EFTbqWSwLBNNK1fBqjlby++y42Ny7nw8rHeLrkF9RbNuvDRQTjlX+dx2TVELU66LSjViMl9X9yTEUNW+Vsq7uRiuDamDhIHFTQhrZ/kpH12M13YrfchzT3rRJ8b3Eg+2gfMXJSEcPHDWDbl13ZOzVd5Zd3n9P+Ob8w+QT+3kuriIZNXB4dl1tn2NgBrP1oa/t5K8ONne4BRdDiD7Pxy137I+YMwNdb93DZJY8wZ944MrO8NNQ7+2tNb2oT4S8unYPbrfP822upbfQzuDCHt5ZvIpykNqENiiqYdtBAbl44j9c+38wD767sUtNQUl3PadPGsqO6nnU7ypO2tbu2kTtejmknuHw6kVxw1bZlUtGxRQFkCtlB1x49k3R37MJHF57OD55/hWA0ikAQikYxHZaaEomtS7rT9lhS8vf1K3vsM2iZ7GjsHksRCFtBhjy406JEbasXGUTOsKXEHw07OJlikFJg2go+o3e0HAA+1UPQCqdYrNbRv0Ay0FPH4j1/JU3zMjz9EEKWn8eLr6bZrMWUYQQKm5qWcmLhNYzJPIoB3rH41Ewa7VCXZxEoBMx6NjXFvg8bGhe3M7lqWIww9uARZrtAVvIhWiidlNWaw2tAapCgPsGWQXY2PU5f31RE9HPoXNgmDITvkpjCXtMNEHyVmItJRbbcj8y4CcUbn977TeKAUdgP+Nur1/Diw0t4/qEPiYZNph0zmp/86SyMTtoKY6cOTdICBJpj/otwMIqqKoybNqTdKEgh2g1CG2I7iN6NU1GI02QACIdNirdX8cSjy+hbkBlnFCSxIHB3umwnDCvKY/rEwQBMHNkfy7Zpagkx97BRPPrSCkp21WLZkgYH+o0Mn4tXb/8B7tbYyUOLP40rcgtGTBZ/8TUf/+EKjvjN/TQFEgdIw50qps1mG7dPYCsSy00XgwD06PO6fMYhfH9Gh/tqQmEBy3/8fdaX7yFqWTzx2Rre+9o5vdd2905YpzscM5qkJF2m8+bcC1hWuZU/bHjLkc+oM/p7stgdjKdj0YTC6YMOJtvlS2ha+mc04tUjvc4zcCkGFw5ewHOlr1EXTV5joKEwLK2ShqiOiiTH8ONWTUwJS6oeY3j6Iayue5Vms6Y9nVRiY8owb1f8jZEZh7GpcSnN0dou7RrCgyVNTLqu1NsK3ca5yvAqkbhsrg5diO50FjblTf+kKOsKAFQlDTuBQWiDZTdjZ96O2vInCL0Va1PtD77LkC33Q/hTkJ3Tc83Yv6abkO5ZCGX/kEGmggNGYT9hwfePZsH3j056zQ9uOJUHbnmlx7aiEZOKkpr2z9LYP0n0V1w1j4fv/wDTtB3Fc8Jhk9Kdte3qaG0ThK1BOMdIuoRyGRoXzp/ORfOntx974Z21PPT8x4QjJqqqsPCEqdxz3ZnYtuScnz9G2Z6OCcrQVR66eWG7QQCobXbesbSEIpxz17MUZKXTEop0qfVQiBEGdtdGNi0bNaQgM0BaqU3SLk1lRF4et544h1GtinqdoSoKkwfE6LxL6hoSGwXXN5PhpwiFPHca84sm8Un1dj7cs5VQEsPQEAmg0H2KA1Uo/GzMPKrDzdyz+YO4+3TFwmv03iAYQueSIWcyu+9hzMydwpVrbqDBdJZjdSsuZvWZRnXoadK1+EK/+khsV7il+RNHvQQJbG9Zw9sVf8eiW1xDhkiUSZCuhPAo0fhnE5CmT0AIjeZI95iITWnD3ylIOwND60OGawrxb7UrVMWHpuYhsv6ElLd2BJibbsQpY6nTnRD+CDwnJ21/f+KAUfgP4tSLjuDgw0fwl589R82eBvL7ZVG6rZKgv+uXPBqxqN7TSE5+OnXVzQg7+SQ28/ARfPbp9qTVyYoiOGzmCA49dDhvvbme1Z8X8/XWSkfj0DbJtuXdh7N0pJZ8Rkj3ubho/vT2IN5byzdy7zPLOlb6UYtn3vgcTVO4+LRDWXTXpWzYWs7y1dsYOiCPeTNHoXTzhx3UL4+NZc4FgN2PCyEYX1TARcdM4ddPv41lx//Q+vjSWDB3Ao9+vIq6cMjxnWqKQFNUjht1ELedfGzy7KVOOG3CGG5d/GGHtnPryzMz7W8kcudSVc4cMQ6IGYc/Tz2TzY0VXL/mZbY0Ob8zv+WcFKEpCl827KbEX8vhfYazZM8WrE6TqK5aSCli2QYpwlB0/jThVwzwxnz9bs3FrRN+zi2b7qEx0gxSEpUWZw44jjOLTgRiGXF/3foyISvecGTqsZRNt+JM825Li92BjY7uL9G6LXTSbXaJqKOAjwBaoutJ9MNThEZ96ONWJlUFXc0jajkX4gncDMy8on3nIYSBNHeC/xGSGwRaF2L/2dDvgUDzfxhFw/ty98tX8/TKG/n5nec41ghousrwsf25/dkfkpWXhiFE0pTJmUeM4K33fsniJdfxi+ucVxQjRvWjT58MCvplcfGlR3HGWdMxUkxx1VJgD21sDtLU0pHC8+iLK+NcP6GwyTOvf95udMaPKOSKhUdy3BFjUBSFXbWN/PSx15jx63uZfeNDjCjMx6WltkuSUiKlzewJw8nPiOeoMjSVEyaP5LIZh/D2lReT5XXH/dyLsjO5fu4xvHDRQu445biUDQKAoar89qRZRHMtzAwbM90mkmd9Y7uEMTl9uHLi9C7HRmf2475DzyHH6B1fR8g0uWzFU9y24W0+qtoW0wbpdD5sqb3SgFZQuHzownaD0IYCdx73HnwzxxYcGVMaFJLX9nzIa+VtoveCw/MWonfL0lHQGOKbRMBsZHzWHLRuqZ8ChRyjEJfiw3aIvjsZgzb4bXcCarvYnYl3AAqqiBkoKSUFaWcBTnrmGkNyfh5XzSxDi4EUAvXSAtdRPV+3H3HAKHyL6D8kn4kzhmG4uk7OuqFx6oVHMGBoH55acQM3PXwJA/olDlQffkRHxeW8Y8dz0aVHomkKuq6iaSqjRhfy+z925Ww6bOYIdL3nCVcQywjqKV8vatpcftOzrNlUBkB1vXNeeiAUJRKNXx3VNgdYeOczfLBhO/5QhOomP2+t+YqDhxYy/aCB5KR58BjJqVM37aqmusnPH887Hq9LbzcoXkOnf04GRQOz+cPipbz25WYeX7iAuSOH49E1sjxuLptxCG9+/wLOnTKREX0SU3Akw/QBAzAMDdstY3GEb+jX5dE0fj71CNxa/Pso8GTy2uwf4VNTUAwCdKEgkYRtk6AVJWybRG0LTVExFA2fZuBWPPjDHtQUHQuDff05us+hjuc+qFrB23uWErRCWNLGbwZ4tvQ13t6zDICpOadyTN9L8XYSyhFCYX3jYu7Zei6vl9+JbJ34BSq6cJNlFHBG0Y0MT5+GmkTYRhU6huLBUDxtd2OJDOotH72RDm8dFTmeI6kLLOXz3cewq/EBYqt+FYEbVfhwa0UcMmAp/TMujBPgignwJFt0qIAbMu9EKGmti55gQnXH/YkDFc3fMsKhKP+47XXeff4zIiGT0ZMHceUtpzN0dFfpycrKRi5YeH+cu+eU0yZz1TXxWgh+f5jibZVk56YxYECOY987iqu46TcvUlPTDEK0E+91htutQ18PDXbU0dUUd72h8eDNC7nt4cVsLo4nvMvPTuOVey+L+5Hc9/YKHn3/8zg6bZem8u9fXciA3Ex+9PDLLNuUWKxEVQQv/fIChvTJobbZzyufbWJXbSPjBvXlkbVrKG9uJhCN4tY0NFXhqfPOZIxDrMC0bSqamsl0u8hw90JMGTjr9WdZU1XepepZVxQEgoi9f/QavJrOP+adxmGFiekRbv3iDZ4vWU2028o52/AStUxsYqmiIzL6UtxcTaBbLEIAC4dMY2ruIGwkM/KH8EHVR7xZsYSAFWSYr4ht/p2OmsuTskbz2zE/dhzX5auupyYSX5meqafzj6m3tX8v/GY99319cVK9ZQWNYWlTWTDwt+33vVfxEJ/Xv+xwtWCobzITs49FFTqDvBOpCm9na929mJGleJSeKrpV1Nb6ByEUxvZ5BCFUvthzLrbs2CELDNKMMQzK/glZ7hmdgtVdIc2dyJqTSJSxhHYwIucBhJKNHXgRWv4Cdj0IH6T9EOG9JKHSY6o4QHPxX4C2LXQi1NU1c8+d77B+fSkZGR4uuPgIZs8Zt8997iqrw7JslnywiRcWfUao1Ti43TqjRhdy/c2nsejtNSxbtY2MdA952T6Wr9rumGIqBBwzbQQL5k3ip7e/1OUat6Fx3eXHMveweAWtHzzwEp9s2Rl3PM1t8Idzj+PoccNYubWUHz38sqMOA8R2BJ/88cpYJXkn/OmD5Tzx+dq4IrDheTm8edmFXY69tvErfvfuh4RNE8uWHHPQUG47aR4+I7WVd2M4xI8+eI2VFWVoioKuKNw8YzZ/Wf0RZS1dubA8msbPpxzB+2XbWVFe1qu0Uk0Ijhk4jD8feRxZ7vhirbqwn7OWPkRDJEDQiqILFV1ReHDG+YzP7s8ufz1R2+LPG99lRXVxXN9tRuH6CSc49i+l5Op1t1AerOrix3cpBteOuJQpOeMd7zvzkx8lTE1VsCh0KxyeP43B3hyWVD2W1ChAbPV/7agXUVsV0uoi5Tyy7Qos4u9L1/L40Ygnuxz7ZOdkLJmYowxAEW7G9HkIy25BES6yPDNQhMHGyh9QF3yf+HiEi2kDFqNHPwdrN+jjwDg8zkDY/ieh+da4+2ONeBB9PkOG3oPGX9O1EtoD6Veh+C5NOu6ecIAl9b8APVn+nJx0bvr9Gfu9z4FFMdnEiy49ikkHD+L119YRDIQ5ZvZYjp41Gk1Tufx7h3P59zo48td/tZuf3PYCwXA3PV0JxWU1TB4zkLt/vYD7n/uI4l019MvP5LIzZzJzsnNq7rCCXD7bVtauwtYG07IZkBtznR06oohrTjq8vfagO3575uw4gwDw+qYtcQYBoLS+kaqWFvqkxXzDn5fu4vo3FhMyO57pw6+LufaVt3jgzFMd++yOTJebfx5/JtUBPw3hEIMzs3h+65dUdqPONhSVmYWDuHDsZB7c8Hmv6wxMKVmyq5jz3n6e1049P+67k+Py8cqsK3i1dD2f1+6kyJfNWYOnUuiNuWVsJOd/9CghK+rYt1vVOa5/Ys0DIQTXjb6SmzfeTVO0BSEEpm1ycuHshAYBoJ8nn93B+EC4gs2kzDJsBFsay9jeLBIW0nWGlDamHUFtpQJJ07JjsVmH15ltFMYds5OS66koQqdf+vlkew6LOxuMbnfsSBEqoepT0BQTZAiEG9ShyOyHEdb22GpfG4PiuwC75V6QDpxeErBroeUeuhoEgCC0PIDcD7sFJxwwCgfQBZMmD2bS5ME9Xjd0YG5c2ifEspxGDo1likwcNYAHbjo7pTeWIDoAABjNSURBVH4XHjGJF1Zs6GIUDE1l7MC+DO/X4eM/76jJHD12GNc+8Rpf7YpVlWZ43dx41mzmTHTmRtISVPlJ6CJU/9CKz7sYBICIZfFx8c4uxiMR/NEIa6vKSTdcTMgrIN/ro7ixjptXfBDnOhIC7jzqBDRFwacnjpXoSoy0rsCXxo7G+i68RlHbZntDHV/WVjE+L55Qzae5WDh0GguHTos7d8eX7+A3w46myK3qnDJwIpNzijqezQxSG64n352DR4251Arcedw3+Ra2NBfTZLYwMm0omUZyYaQLB53On7c+0s3tJCny1KKIGEU2oqtUTjIYqreLHrOheJiYdSzrG97twqaqCRcz8xayqXEpK2teIGA1MMg3CZ90oQtnwzAg43/bO/M4Kao7gX9/1Xf33AcMc3Arx4iIiIiioGHFC9Soa1Qwq6uSGF2PxN01Mdlk3d0kuutmNTGrySYxalZhjYZEEzxDjILRBESQQ3EcBhgGhplhjj6mu+vtH9XTM30NzTgzPcj7fj71aeb1r6pePbrqV+/9rpsYlXdRxiyp+a6ZBCL1pMQxqABugn1q/fghshUOnIkStyVvlELxj8AxFbrT1FgQAaMMohkCNFUnlrLov/DQQNBKQTMg8n1uLvnMiax+9b0ELyOnw54Qq5At1aWFPPrFy/jnlS/xUVMrhgiLTjyOe644J1W2rJCnv7yMrmA34WiUIl//N8ZlM2v57zf+lPDAN0SYNrqcEl+vp87uQ+mXERw2Gwc6u/pVCk9t28Q/rXsFh2EQVYoSt4efn3cFv9r5flJBHAul4MX6D7ji+Bl8fvrJfOfttb3urFhpME6tqOa/zr6IUreXFS8/x4dtLSnHMURo6GhLqxT6452D9WkfugI8Om8Zs0ste0VUmfzPRyt5df+b2A07UTPKeRVnsXz8pRhiICJMLUjNGpqJ2SUzOLFwKn9ufS9+/lJHB6XO1NgEAzsiEFWZ3TZDUT8rd32Dq8b9W/yteVHFTdgNJ39peZ6oCpNnL2FRxQp2B95nffNKwjFlseXQa9gYxSy3H5eReA7BQVXh3+C0ldIWfIt97U8RVV2U+y6kzHcBhjioKbqZZv+LmKo3nsYQN2OMblI9uGMvBSrmgBENoFo+D4X/Cd0bSFke8t1oua7aJ1oKJRnxojp/Cs5acJ6Z0XYxELRS0AyY25afTXlJPk+98GfaO4NMm1TB7csXMr6qdEDHmzl+DM/8/bX4Q904bDYch3FH9bmzW+e/Ye5s3vq4gY179xE1TRw2A5/TyQMXn58gd2pNFfUtrUSSA99Mkwkl6Y31AJsO7OOf1r1CMBohGLv3A51hrvntSi6aMCVtrYJuM8ovtm7iiuNnsHzaSbx7oJHn63bgMAxMpajJL+Shs5dQ7rXca08ZXcUf99QTTCrrGTZNaktTjeU9rD/wET/cvpaGrlZOKK7klqlnc3zBaHx2Z9pAN6dh56SSmvjfKxue5/cH1hNWEcKxc69pep0iZyEXVy3KeN5MNAWbebdtW4JCyrMnRhP3YDPszC25nHXNK9PaCAAUUfYGtrMnsI1q7zQA2sPNKKWo9kynwjOZOSWXYDecrN5zX4KNQmESxcbuSCmTnIlLWh7HRJy2Uupbv8/u9kdixmRFW3Ad+zpWMqPiMbyOicwc8zR1Ld+lI7QBu1FEVcE1VPi/x2HjD1CgOhGiUPwIquM7VtI8owR8KxDvMgAk/y5U682kLCGpEHQ9iPK7wTYOSp5EMsRwHClaKWgGjGEIy5bMYdmS7LOXZoPXld3DPlucdjs/u/oyNu5p5L3GJsYU5LNw8gQcSUntVpx+Kr95fwdd3d2YsWUaj8POzWfMxduPO+zjWzem2CwU0N4doqagCHuGGgybD+6joeMQNfmF/OfCC7lj9hm819xEla+AmeUVCevFV0+dyY83v0M4GI0vIXlsdj4zbhLjCtKnQHhh93t8fePq+MN/f2M7b+7fyePzr+eqCafy4x2vE+wT5Ocy7CytmYkt9taplOL5xtcImYkP5JDZzeq9L2dUCkoptnXsZHtHHcXOAk4rmYUr5iL7fvuH2MQg3EcrtIW9lDi7UlJXmyrKrJLzMInwp4O/zGh0NlWUxsB2qr3T2OPfxv/Wf5WoimASYVdgMxvbfsv5Y27DEHtKUjqFosMsxJBOTNWFIR5EHEwtf4BQpImGQw+j+igkUwXo6H6Pg/5XKPMtJs85jRkVP0vsT3gNRDaT1QKYakPcixFX+kwH4poPxQ+jOu6HSF3smN3E8ycpP0R2ojq/jxT84+HPlwVaKWiOCUSEWdWVzKpONTb2UFlYwHN/ew0P/WEd6+obKPN5uWneHM6f1murMJXi7V27aWg7xLTR5dRWjOZgwJ/WWCtAhddHmdtHoz81Stdps7O5uYmafMuQPja/iLH5RSlyYBmwn7/kWu5753Ve3bUTr8PJ8mknceOM9ArZVCbf3bwmYTaggEA0zAPvv8wPT7ua+s6DrNm7BZdhp9uMMrd8Av9wwuLeY6AIRNPnFO+IpK85HTYj/NvWh9nR8RFhM4rDsPOTulXcW3snY32VFDryU4yjhyIe2sNuChzBmGIQ7OJkfvnVeGwF1kt1PylhbYadPHsJ65ufYe3+xzD7vKVHVTeBaJh3W9cQVemDxSq8pzKldB7toQ147GMpz1uC3chnf+dqRByoJEViKj8H/S9R7DmLlsCrRKKHKPKcjscxHgAp+nfUwc8BIVABwIEVqJb0G1FhlFEGofXgmIoY6f/vxTUfcc1HmZ2o/aemHoduCK4GrRQ0msGnpqiQ+5amxn0AHOzys+yJVTR2dFgPKhSzqipZdNIk3mzcRSCS+NAJm1HmVFRz/oTj+NmWDSmKw1SKyrz+DbN9qfDl88CC9C6iybSHg7SHU5MOAmxu24PdsPGd2Z/ljumL2NlxgLG+Eqp9iTMOmxhUeyrYHUiNN5noq0lpA/jdvrVs69gZNyRHzSiYcP/2H/HgrG8ws2gqLsNBMBpMyGHaEKzi9uoL2R96D4fh5sSiv6LSM4VnG77Nh51vEc2wHCMIDnGzsXUNDf4tCQqhF0VDYAvjfSfxcdfGBOVgFxfzyv6aMu90ynyLE/ayGXkZPKBsmKqbtxrmYf0KotCqqMj7HBNL7kHsE2DU7yH4O4juRdmnQOdDsTf9HiXrBqMYWq5DiQNUN8p3HZJ3xwA9igYvtGDIIppF5GMRUWm2S9LI3igi20QkJCLbReQLQ9UvjWagfPX5F6lvbcPfHcYfDhMIR/jz7r3sa+pgYmExnj4V0jx2B7fNOp0St5e/qT05JV2HXQzGFhRxYlnFkPTVa3dmdOksc/WuPY/2FHD6qEkpCqGHGyZeidNwxI8kCC7DyXUT0rtGv9L0ZtqAtubuFppCB7GJjW/V3s4oVxkuw4nH5sZn83Dn8Tcwt2wxS6q+wnljbqHSM4W27iY+6FyfYdnIqrY22j2Zc8fczJ7A+xntDmBVXLu46h+YnHcqNnHgEBceWwEXVt5BtXd62n2KPfNjkcfJZ7bTFvwjUdVJVHVhqiCmCrGvcyWtgd9bMuJBPJcieV/CcC9CSp+G/LvAMRucZ4N9EpjNQNCq10wIuh6D4G8yXoMYeeCYQepj2wnuwUuYN9QzhTXAN5Patvf9Q0RuBB4Bvg28DHwGeFhERCn1wyHun0aTFcFwhNc/qk/xJApFIjy7aSuvfOk6Vu3YzPN12yl0ubl2+izOiEUcjyso5ifnXsZX/vBbmgN+lFIxz6ILh8TPHCyD8eXjT+aZ+r8kGKc9NgcrppyV9XFmFE7h3hPuZFXDC+z2NzLeV80VNRcw3ledVj5TPQZB4ktA1d4x/ODkb7HLv5eQ2c1E31jsRurDtzlUj10caZd9qjxTuazmHnz2Yt5p+XW/tRrs4uTk4gtx2bx8tuZrBKOdBKOdFDjKMfpJi2GIkxNGP8aWputj8QyCIkJl/rU0djyZIm+qAI2dKynxppYEFXEjvuXgW44yW1D7zyI191EA1fUTpJ+MqFJ4H+rglVjKxA/iBVs1kvd3Gfc5UoZaKTQrpTJWEhERO/CvwONKqa/Fml8TkUrgXhH5sVIZFgI1mmEkYiaXve+lOxrBbXewfPoslk+flVbm9MqxvHHlTezzd+Kx2dNGIQ82d9Uupjsa5VcN78bjMW6esoCLqk88ouNMzhvH3dO+mJXswvK5rNz9QspsochRQIW7t2qeiDDOV9XvsYqdlWndUQ3sjPEcj89uzW7y7aXYsKekzLZkbUzOm8tpZb25v9y2PNy27Dx18l0nMLfmTQ4F38FUAQrcc+gIvUtjxy/Syqt+g+FimB1YuY3SfdeGCm+FyA6wjQfHiQkvDmIfB6Neiy1N7QH7dHAtSDujGSi5tinMA8qBJ5LaHweuA+YDrw13pzSaZPJcTqaOKmfLvsT0yDZD+Mxx2fnpiwhjfNnbED4pDsPGN09awl2153Kwu4sKdwFO29De8hdWnsOfWjaxy7+XoBnCZTgxxODLU2447KwoqiJ80LGepsBOil2VTC04k2pvLQ3+zQmzBZvYmVPSG2E+OX8ODsNNOJpabW1p5d8zrSixTveRImKnyNOb4K/ANTtt5lVDPIzyLT38AW3VVpSzSrb52ICINRMQG1YhnklQ8jPE6C3JKuIBz6UDu5gsGLLcRyLyMVCMZXq3ARuA7yilnusj8wXgh0ClUqqxT/sooAm4RSn1g/7O82nKfaQZ2WxtOsA1T6wkEo0SjETxOOwUuN388rqrKc9LTdd9rGIqk41tW9nWvpNSVxHzy07BZ+8/nXcg2sHP6+6kI9xCWAVwiBuH4eKqcd/m7YPPsaX9NUwVZZR7AueNuZVKT2L0ekv3Xlbt+iYt3b1lcSXmxfS5cf9CtTdzyo6BcKBrDTuav4xSURRhDPFS4JrNCaN/hMjhFa8ZeBEOfQXLtdTESrvdUwKpr23EAe7FGEUPDGr/IQcJ8UTkIeBtoA4YDdwCLACWK6WeiMl8FWv5yKNUb6rB2LJSGPiGUureNMe+CbgJYOzYsbPr61MTqWk0Q0FLl59Vm7ZQ19zCzKoKlp4wLetkeZrM/K7x+7zb+mKC95AgVHtrWTb+PkwVxVQmdiNzvMj/fvxVPvZvTGkvd43nhkkPD3qfg+HdNHU+Q9hspcRzNsWeI4ssVuHNqK6fQrQBnKdD1+NAusp0DmT0pkFdIoJPmBBPRBYBL2UhulYptRBAKZWQO1dEngXWYxmUk5eLjgil1KPAo2DNFD7JsTSaI6HE52XFvMEN1tPA1vbXU9xJFYo9/q2EzSAOw92vURhgd+D9tO3NoXoiZrhfhTIQ3I5qxhXfNuD9xXECUvQf8b/Nrp9mkIzGtsFVCpnIdoHxTWBaFnLpi+oCSqmoiKwCvisiY2LLRT3pAYuBxj7iPTkFUpO9aDSaEccu/16erP8VOzrrKHEUcnn1+cwrOznr/fvPiJqdh5bL8BJJU3LUJo5+i++MGFxnQuhlUqq9OWYiMnyz0ayUglLKD2wbxPP2vN1viX3WkqgUehyH06t+jUYzYtjtb+TuTfcTMq2sq+3hTh768Oc0d7expDI1oWE6phcsZGPbbxMMyoLBWO8MHIarnz17OaVkKW80P52UHdXJzKJzBzVh3FAhBXejmt+OGaCDgAvEiRSkrKAPKcM2UjE7wZXALqVUT4jkOqAZuCZJfBnWLOGN4eqfRqMZGE81PE/I7E5w2A2Z3Tzd8BvCaQLZ0rFg1LWUOmtwGh4MbDgND3n2Ei6suiPrfpxWdgW1hQuwiQOX4cMmDiblzeGc0Tcc4RXlBrFVIeUvQd7t4L7AqrBW9iLiSJ8SfqgYEv80EbkKuBh4AWjAMjR/CTgZuKpHTikVFpGvYwWr7cEKXjsHuB64VSUnHdFoNCOODzrq0sZwKKU4EGql0pM5i2sPLpuX6yc+SF3XBvYH6yhyVnBc/tyEWgmHwxAbF1TezoJRn+dgaDdFzjEUOAZWbztXiFGA5F2f0z4MldNyHTAKuB/LPtAFvAOcp5Ra01dQKfXfIqKALwN3AbuwXFEH311Ao9EMOuWu0rR1l6MqSpEj+7gMEYOJebOZmDf7E/XHZy+OB7ZpjpwhUQqxKObsFhMt+UewUl1oNJqjjMtrzuO+bY8mpNh2Gg7OKDsFr33oI7c1g8vIt75oNJoRzUlF07lp4lXk2304DQcOsbOg7FRWTMyuFOunHaUUKvQG5qFvYXb8OyryYa671C+5TnOh0Wg+BSwcNZczy+dwKNyOz+aNF9U51lHKRLXdDqG1QACwobp+jir4Gob3ylx3Ly16pqDRaAYFmxiUOIu0QuhLaC10/wFLIYAVhBaE9n9BmW057FhmtFLQaDSaIUIFX7BSXCcjdgi9OfwdygKtFDQajWaoEDfpI7IFJLugvOFGKwWNRqMZIsTzWcCd5hsFrjOGuztZoZWCRqPRDBHinAV5K7BSY3tAfCBepOhhRNIpi9yjvY80Go1mCDHybkZ5LoXQH0E84DobMUZu/Q2tFDQajWaIEdsY8F5xeMERgF4+0mg0Gk0crRQ0Go1GE0crBY1Go9HE0UpBo9FoNHG0UtBoNBpNHFHq6K57LyIHgPpc9yNHlGFVrtMMDD1+A0eP3SdjJIzfOKVUeXLjUa8UjmVE5B2l1Cm57sfRih6/gaPH7pMxksdPLx9pNBqNJo5WChqNRqOJo5XC0c2jue7AUY4ev4Gjx+6TMWLHT9sUNBqNRhNHzxQ0Go1GE0crBY1Go9HE0UrhKEREPhYRlWa7JI3sjSKyTURCIrJdRL6Qiz6PFESkRkT+T0QOiUi7iPxSRMbmul8jCRFZmOH31ZYkVywiPxaRZhHpEpGXRWRGrvqdC0SkWkQeEpF1IuKPjdP4NHJuEblfRBpFJBCTPyuNnCEid8fu8aCIvCsilw3HtfSglcLRyxpgXtK2tq+AiNwIPAI8A5wHrAIeFpEvDm9XRwYi4gVeBaYCnweWA8cBr4nIyE1wnzv+jsTf16KeL0REgF9j/a5uBS4DHFhjWT38Xc0Zk4G/BlqB1/uR+x/gRuAbwEVAI7BGRE5KkrsX+CbwfeB8YD2wSkQuGNxu94NSSm9H2QZ8DDxxGBk7sB94LKn9J1iRlI5cX0cOxu02IApM7tM2AYgAd+a6fyNlAxYCCljUj8zFMZmz+7QVAi3Ag7m+hmEcK6PPv2+Ijcn4JJmZsfbr+rTZge3A6j5to4AQ8K2k/V8BNg3XNemZwqeXeUA58ERS++NAKTB/2HuUe5YC65VSH/Y0KKXqgDewHnKa7FkK7FVKvdbToJQ6hDV7OGbGUillZiG2FAgDT/fZLwI8BSwWEVeseTFW3c7ke/YJYIaITPjkPT48WikcvSyJrWGGRGR9GntCbexzc1L7ltjn9KHt3oikltTxAGtMjsXxOBxPikhURA6KyC+SbC/9jeVYEckbni4eFdQCdUopf1L7FiwlMLmPXAj4MI0cDNNvVCuFo5NfY63jLgauAYLAsyKyrI9MSeyzNWnflqTvjyVKSB0PsMakeJj7MpI5BPwH1nLIOVjr3IuAdSIyKibT31iCHs++HG6sSvp8tqnYmlE/ckOKrtGcY0RkEfBSFqJrlVILAZRStyYd41ksg9S3SZ16ajRHhFJqA7ChT9NaEfkD8Ccs4/M9OemYZljQSiH3vAlMy0IueeoZRykVFZFVwHdFZIxSqpHeN5NiLE+HHnreNlo49mgl/Rtspjc5TQyl1F9EZAcwJ9bU31j2fK+xaAXGpWlPvhdbgSIRkaTZwrDes1op5JjYOuO2wTxk7LNnHbKWRKXQsy75/iCe82hhC722lr5M59gcj4HQ9/d1bprvpwO7lFKdw9elEc8W4FIR8SbZFaYD3fTaELYALmASiXaFYb1ntU3hU4CI2IErsW7GfbHmdViup9ckiS/DeuN4Y/h6OGJYDZwmIhN7GmKBRmfEvtNkQEROAaZgLSGBNV5VIrKgj0wBsAQ9lsn8GiuG44qehj737ItKqVCs+XdYXkrp7tnNMU+5IUfPFI4yROQqLJe/F4AGYDTwJeBk4KoeOaVUWES+jhWstgd4GctoeD1wq1Kqe7j7PgL4EXAL8CsRuQfrrfderHF8JJcdG0mIyJNAHfAXoA2YBdwN7AEejImtxnrxeEJE7sJa+rgbEOC+4e5zLhGRy2P/nB37PD9WEfKAUmqtUmqDiDwNfE9EHFhj+0WsGJm4AlBK7ReRB4C7RaQDa/yvxLpvlw7T5ejgtaNtA07DisptwnqraMN64C/OIL8C2IHl6vYBcHOuryHH4zcWK8K7HegAniMp2OhY37Ae7puwvJDCWErzUWBMklwJVjBkC5bN6xVgZq77n4PxUhm23/eR8QAPAPuwvAXfAhamOZYNy5BfH7tnNwGXD+f16NTZGo1Go4mjbQoajUajiaOVgkaj0WjiaKWg0Wg0mjhaKWg0Go0mjlYKGo1Go4mjlYJGo9Fo4miloNFoNJo4WiloNBqNJs7/A91VHRQJHkqVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pca of all embeddings\n",
    "pca = PCA(n_components=2).fit(embs)\n",
    "xs, ys = zip(*pca.transform(embs))\n",
    "cmap = cm.get_cmap(\"viridis\", 12)\n",
    "colors = [cmap(len(m.edges)/max_n_edges) for m in motifs]\n",
    "#colors = [cmap(len(m.nodes)/max_n_nodes) for m in motifs]\n",
    "plt.scatter(xs, ys, color=colors)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we investigate a bit the dependence of embeddings on graph size, in terms of both number of nodes and number of edges. We see that the minimum embedding norm for each graph size scales roughly with size, which makes sense given the margin-based objective. Additionally, there is some variation within each graph size, suggesting the model picks up on more than simply size-based cues. There is more variation when measuring graph size by number of edges, which makes sense given the node-induced subgraph setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot norm vs number of nodes\n",
    "xs, ys = [len(m) for m in motifs], [np.linalg.norm(emb) for emb in embs]\n",
    "plt.scatter(xs, ys)\n",
    "plt.xlabel(\"Graph size (number of nodes)\")\n",
    "plt.ylabel(\"Embedding norm\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ilGLUkwmqM/uqpBea79JTr1LQNbsRiLOV1NtvtFb4KpX/AT4nIr8Lcn4ZxqAmnw0henOJU4oTdxtV1SfbSrtmjxyRYW2ZSRQbhfa2wWkr8cVXqYwG9gKeEZH7cDEqYaxGvTGoKNcrrdqzr2KUYCnUmY28YmRahOknDk5biS++SuXS0N/vitluNeqNQUWpLsaVwGdZqxwl6DN+I1UmLJZGTkNfC6xGvWGUQDFeaZW0Zfgua5WqBH0i1Hdub6O9iZe/GjkNfS0wZWEYJTDlgDHMmDqBMe1tCO5pdsbUCQOURfam3NHZhdJ/U567uCMVOXzjQ5LibCbvPZpJM+cz/sJ5TJo5f4BcSePfsHBFzndKUijv2nEbWoMyiK0ipFgQsWhaSyjHmLE7ZNEUVaPeMIx+fOwilbZl+C5r+Xihxc1yynX73billxdnHNv3edyF88oarxxKCY685iPvrYAkzY0pFcOoIJVOM1PMslZUCU6aOb+gwiu3WFWasSjDhrSwtUcLpmTxJZoMFOojHqrRMaViGBWkmJt+KbaXcjIO+Ci8uPGLIU3Hhc1b+1O6pJGSpUd1QLyJKZHyMaViGBXE96ZfahxJORkHfBRe3PidG7ewYUthJRO22WT3NZofUyqGUUF8b/rl2F5KjXlJKjw2ee/Refc76X1jmP34ypza6plW4bT375rjERZnszGaH1MqhlFhfG76+RIyVoqkwmPz/vxqn3LYvi3Dhi1b+xRIR2cXtz7RMUCBxCnKOJtNIVpFaBGlGsUZS/EGMwrjpVREZDnJJQl6gXXAE8D/qurTKclmGIOGJMNzJW98STaVtRu7+1yEO2OCGru6e1jw3OqC8RulGOl7VAfUV6kUZxy8a3UONMjw9cJ+EGgFdgKWAwuD951xiukV4ATgcRH5QL6BRGQXEfk/EXlERDaKiIrIuJh+mvB6b6Rfi4hcJCIvi8gmEVkiIid7fi/DqAuSDM+VrBFSjo0jbgaVzdqcjXnJtJamECv1jcPxMmcdMparpkyo0JEGN77LX3/Apb8/WFVfyzaKyE7APcDvgI8BDwCXA0flGWtPXF2WJ4JxP5Sn7/XATyJtL0Q+Xwl8FbgkGPN04BYROV5V78r7rQwjZUqNnh+TYDRvb8tUrB5JOZ5dAjly1bv9pNrlpwczXjXqReQF4GJVnROz7VTgW6q6p4icAfxYVbfPM1aLqvYGf38auA4Yr6ovR/opcLWqXjpwlL4+OwIrgZnhhJYi8gAwWlX3K/TdrEZ9/VPJNCdpklSPPi7S3mffTIuAkGsQbxG2HT6Ezo3dqZyL6LndsHlr7JJXIxGtV+N7DYziSKpR77v8tSvJ1R43Admr1QEMzTdQVqGkxNHB8W6ItN8ATBCR8Skey6gBlU5zkialltSF+LQv2w4fkqNQwNVuX7uxu2Ln4vj9dxqQzqWRaMu0cuYhYwumzzEqh+/y17PABSJyr6r2KRcRGY5beno2aNoZ+HuK8n1WRKYBPTg7zmWq+ofQ9n1xyu6vkf2WBe/74Gw/RoMQfXLeuGVrRdOcpEna6fDHe6Q0KedczF3cwbQ5S3I8u2Y/vjLHs2tIC1XxxCoHyyJcX/gqla8BdwIrROQu4HVgR+BYoD14B/gAcG9Kst0QHPNvwG7ANGC+iBylqr8P+owCOnXgGt6a0PYBiMh5wHkAY8eOTUlco1ziAgCTqMdStGmnw/dNkVLqubj8jmUDZ0I9yrw/v8ribzpT5x4X3UXlTOfl0ypitpI6w2v5S1XvxxnqHwAOA74QvN8PvFdVHwj6fVFVz0tDMFX9mKrOVtU/qOoNwAdxCuaqFMa+VlUnqurE0aPzB3oZ1SNu+SiJNKOzo15LpS4nJQUNFgomLHa8KKWei6TMwuH2SnqfpYG5Bdcf3sGPqvoM8NEKylLo+G+JyDzgU6HmtUC7iEhktpKdoUQrVBp1jO8Td5r13NMss5sUTJjUXup4YSpV2z7r2VVPvGO7obyxvrsvoeQZB+9qbsF1SCNG1IeVxzJgGLAHuXaVfYL3Z6ollFE+Scs97W0Zthk2pCLr5sWkRynkhVaMTaWcao3gPJzC2XXTdjuuN5fgSXuMYta5h9ZaDMMDb6UiIocDZwBjgeGRzaqq/5KmYDHHfxtwPPBYqPluoBs4Excfk+Us4GlVNSN9A5GUfHH6iftWzPjqqwh8ZjRJ1Q/bR2S8xopWUxwxtDU2cePIEZk+m4fvWHGKpkWoWvR6sVhcSePim6blM8CPcMtJLzDQvbio0FkROSX488Dg/cMishpYraoPishXgb2ABfQb6r8KvBOnQABQ1ddF5DvARSLyFvAkcBpwJHBiMTIZtaecjLul4mtc95nRJJkfou1JY81auKJvGp5vphAez3esuCW9elUolVrSM6qD70zlAuDXwCdVdUsKx70l8vmHwfuDwBHA88BJwWt74B/Aw8CnVPWxyL6XAOuBL+GUzvPAqap6ZwpyGlWm1Iy7peKbmt5nRrMuIWiws6s7Z3kqSWH43uPDx0mSKzpW3JJeUhR/rQgv6ZlbcOPiq1TGAL9ISaGgqnlnNqp6B3CH51g9OI+wsr3CjMGH7+zIZ0aT1Efon3l0dHYNiPguFp9jxhHtl5T6vlYsjxTMMhoT3zQtDwH/T1Wvq7xI1cXStBg+JKVRCadMiea/goEpQ5LafftlWoVthg5hXVfyMZPIzgSyynPths1srJPIxhaBl2aYUmkkyk3T8kXgyyJyWLpiGUZjEE2j0t6WASEnZcqtT3Rw8oFjclKEJD2yabA92+/MQ8YOSI8STTkyckQG1C2nhY/5vrHb52TgTSK7T/a9XhQKwEcPtiDkZsF3prISeBuwLbARFx8SRlV1t/TFqzw2UzFKYdLM+bHLTlGvJd9+UNjNOGmscpfTakG2fozFmzQuSTMVX5vKAzTe79YwUiV800/6Z4gazn0dAaCwk4KvUb4ReHHGsYU7GQ2Jl1JR1bMrLIdh1DVxNpU4oq7IabpJF2OUr2fa2zKFOxkNSyNG1BtG1fHJS1bqDMSXuFlPoy19ZVqE6SfuW2sxjAqSqFRE5OPAPFV9M/g7L6r6q1QlM4wySbO4l2/KFN/xS5FtygFjWPTKGm58dGWfPWL30SP4y+sbivkqVSXTAju+zVLTDybyzVSuBw4B3gz+zocCplSMuiHNRJGQvPRUSjqRYmS7dO7SPiUigIRSq/So1rVCaRG45iPvNSUyyMjnUjweeCr0d77X7hWU0TCKppwqjHFMO3qvWJffUtKJ+Mp26dyl3LBwRV/6eaV+U6tkCbtJf+dUUyiDkcSZiqq+Eve3YTQC5VZhjBK39HTygaXZSnxlu/HRlSXJWissCaQB/sGPhtFQJBWuKrWg1dzFHcx+bGXfrKFHldmPrRxQ0Mun4JevbPVeICuMJYE0siQqFRFZLiIv+b6qKbRhFCJpuWry3qNLqvI4/fZldEfWnrp7lem3L+v7nLWVhKPWL7pt6YBj+FaIzBcdX2tGZFpylrpmTJ1gS10GkN9Q/yC53or/ArwDly3478Hfk4DXcMGRhpFImp5YPsQtV71v7PbMfnxlX132js4ups1Z4lV7pDNPBuIsvgW/bntiVexYtz2xKiey/JDdR/Lwi/VXvDTTKnxr6n6mRIxY8tlUzs7+LSLnAQcDH1DVVaH2XXGFsh6poIxGg5O2J5bvMW99oiNnuSruBt3dozmZesuRzddWkpRzK9r+8pv1FehoqekNH3yDH6cBF4cVCoCqrhSRy4FvAU2XwdhIh2JK9lbymL7EyTYyoarjyFBVxyS34+3bMjn1VHypp+j5cLVJw8iHr6F+F2BTwrbNuHorhhFLuZ5YPsbvUsf23f+yE/Yl05pr48i0Cped0B8dnmQreWvz1hw7iy/1YlOJfk/DyIfvTOUZYJqI3KeqfcpFRNpws5hnKiGc0Rz4luyNw3fpLGqz2b4tk2gH8ZU5jE8OrwXPrY4dq6eI4JLwjKZW3l/v2nEbNm7ptSh4oyR8lcrXgHnAChG5i35D/bG4cr8frox4RjNQTKbeKD5LZ3GKJ9MqZFpkgMeWD2lnES6GcIXIWjBpj1HMOvfQmhzbaA58sxQ/ICIHAJcC/wzsBLwK3AtcparPVU5Eo9EpJ1Ovz9JZnOLp7lFGjsgwYuiQgrXhwbnFhmWD3FlDti3fdyh3dlQLLGDRSBvvLMWq+ixwZgVlMZqYUjP1+iydJSmezo3dOcblpCJXYWM7wKJX1uSU6O3o7GLaLUvopX8pK+uODP1KsxgTyNBWoaeXPnfnWix1WcCiUQksot6oK6JG+cl7jy6Yc8s3Qj0uIDLTKqzflGtIn7VwxcCZT68OsI109yiX39Ef/BjnHZbElh7lndsPR4B3bj/ce79ysYBFo9LkS33/8yLGUVX9VAryGIOYONtItu57vuBEX5tN3DLchs1bByxZFTNnCCuSYmcctbCf2FKXUWnyLX8dSe7/VzvOKL8Vlw7/7cH+6xhYs94wChL12Nq4ZWusUX7Bc6vz3gyLsdlEl+HGXzgvpW9T/7m6thnaWriTYZRJvoj6cdm/ReSDwI3AZ4BbVbVHRFqBU4BrgLMqLKfR4EQVyOS9Rw+wWyQRZzOJS/tSylN4e0JQYym0SP2mpm9tEa4+aULhjoZRJr6G+u8AM1T15myDqvYAs0VkB+C7wEEVkM9oAuKWtWYtXOG9zBS1jZST9iWqjDaVGHWfRcTNdnZub6s7hRL1aDP7iVENfJXKBOCvCdv+ArwnHXGMZiTO5df3/htnG/FN+1LO7ChKUi347IpXPaVUyWL2E6MW+CqV14BTcXEpUU7HBUMaRizFBAW2t2XYZtiQvDEjSTfw8HHKnR1FqbNJSEGibtKGUS18lcp3gf8RkZ2AW+iPqD8VOBr4cmXEM5qBJEUQffpvy7Qy/cR9B8w2osohadYQXiYrZ3bU6FiuLqOW+EbUf09E1gOXkZuSZSVwrqoW435sNDmFlp3AKZBCrsKQrBziFFJ4mayY2VExkff1yNBWYfR2w81+YtQFxUTU/yyIXdmF/jQtq1Tr3I/S8CKtIlqlxpokkaQclPyGaF+vrrZMK5edkDs7Gpeim3GlaRH49in7mxIx6gZvpQIuwhE3O1lZGXGMtPFRFmkW0UoyoheKNUkiaeZQKGdV0qNOW6aFUdsMK1l5ZhVZrZ+krGCWUa94K5UgoeQ3gMNwgZAHqeqTIvIt4CFVvbtCMhol4qss0iyiVW7tlCi+0fJR5ZmU2HFTd29B5TbGQ5HVcjYztFV44epja3Z8w8iHl1IJgh/vB14Cfg18PrS5F/h3XFlho47wVRZpKoJyaqfE4RMtX6oxP7x/ePxxb4//DklFuKqJ4Ja7DKNe8Z2pzATuAaYAreQqlSeBj/seUER2Ab4OTAT2B9qA8ar6cqTfcOBKXLR+O/AU8HVVfSjSryUY7zPAO4HngStU9VZfmZoVX2WRpiKYdvReTJuzhO6e/lt6plXKyoZbKMOxr6dXpmWgHHEKKclQf+OjK/rsQtWMnrcgRqOR8FUq7wOmqqqKSPRf6Q2gmEe4PXGuyE8AfwCSCl//DDgOV1nyJeBzwD0icqiqPhXqdyXwVeCSYMzTgVtE5HhVvasIuZoOX2VRThGtWKK/kArcfC+du5QbH11ZXL6tmNT0xdSy79H+IMdquqdYEKPRSPimvt8EjEjYthMuqaQvD6nqO1T1WFzMywBEZH/go8BXVPU6VX0Ap4hWAFeE+u2IUygzVfW/VHWBqn4GWICbXQ1q4lK9J2XvnTF1Qipp0a+55/kB1Ra7e5Vr7nm+6LGSuHTuUm5YuKLoBI7dPQPlSKNaYyWxIEaj0fCdqfwR+LKI/DbUlv2P/hQw3/eAqtrr0e1EoBuYHdpvq4jcBFwoIsNUdTMu8HIocENk/xuAn4vIeFVd7itbs1FO9t5SSdtQH8eshStK3jcqx4ihrWzYUl7+r0phQYxGI+KrVL4BPAwsAebgFMonROQ7wIHA+1OWa19guapujLQvwymRPYO/9wU2MzAvWbZy0j7AoFUqkJ6yyEfY0N2SUFPEx0Duay8oZ+UpKsfGOlMoZj8xGh3fiPolInIYLs39JbjV6c/jbCKHq2p6axuOUcTXaFkT2p5974wJwIz2yzoooP8AABzeSURBVEFEzgPOAxg7dmx5kg5yoobuOIWS5AKcVmyML3FyVMM0ko0pyVcYDKxevNEcFBNR/yTwL4FXVvZmHp1JNASqei1wLcDEiRNrHcfW0CQZultF6FUtKv1KqbExSYzItDAyEui46JU1XHDzkr7a8NXgzEPGctWU/lomUYUKVi/eaB6KiqgHUNVNItJdYYWyFtgtpj0781gT6tcuIhKZrUT7GRUiyVbSq8rymccVvZ+P7eWsQ8Zyg4ddZWN3LyNDn29ZtIKHX+z/SVSjUmOLwMTdcifMxdi6DKPRKCai/nCc59VBwFAR2QI8CnwzGjuSAsuAk0RkRER57QNsod+GsgwYBuxBrl1ln+D9mZTlGlT42DxKjXEpJzZm4m6juPGxlfR4BIqE68DXIlFkrxI7+6qGrcswaoFvRP1HgJuAF3B2lb/jAg1PAeaLyOmqOidFue4ALgc+AvwykGEIcBpwb+D5BS6Kvxs4M+if5Szg6cHs+VUuvjaPUmNckvabvPfonNop2XHCym3D5q1eCqVeqHe3ZcNIE9+ZyhXAPGBK2CVYRC4DbscFIHorFRE5JfjzwOD9wyKyGlitqg+q6mIRmQ18V0QyOA+uzwLjcQoEAFV9PfBAu0hE3sJF958GHIlzSzZKxNfmUepSTtx+cZUZp92yBIS+CP1GS0sPpaeoMYxGxFepjAfOj8aYqGqviPwQKDYlSjTo8YfB+4PAEcHf5wBXA1fh0rQsAY4JHAbCXAKsB75Ef5qWU1X1ziJlMkIUY/ModSknut+kmfMHKLJoIGWjYQZ4Y7Dhq1T+QnIqltEk16+PRVULut2oahdwfvDK168Hp3iuKkYGI5eo/WT7tkys22upT91x9hnInak04iwkyqQ9RvHym11mgDcGLb5K5RLgeyLyrKo+nm0UkYOB6cAXKiCbUSXi7CeZViHTIjkzhVKfuuPGnzZnCWj/TCRfZuFGoFWEMw7eNcd12DAGI4lKRUSiHl3DgYUispL+GvW7Aq/jkj7aclODEmc/6e7RAWV2k56648oHh6s8btyyNXb8KI2qUF7O4zptGIONfDOVXnL/z58LXlmWM8hToNSatEoAJ9lPOjd2s/ibSUmk+2WIzkLCMSTNsKRlGIY/iUpFVY+oohxGkZST5iRN+0kxqeMbiWFDWthh2/5o/LUbNrOxe2AuVMsibBi5FB1Rb9QHpaY5KcZ+EhczEh27WWMwtmzNLTs8d3FHbPExyyJsGLkUE1E/BDgUZ0cZHt2uqj9PUS6jAKWmOfG1n8TFjMTNhJrFaytKdJZmqVUMww/fiPr3Ab8BdiG2fh4KmFKpIL5LVtu3ZfLOLpIUwNqI/SQuZiRuJjR579EF83A1mldXkpebpVYxjML4zlR+jAswnIIz1m+pmETGAHyXrDItwoYt/WnV42YXrQn1TqIZe31nQgueW51X9qTjJTFsSAubt/bbLlrFlfGtNFbHxDDSwVep7IOLUh/UNd9rhe+S1cYtW1m7MXf2Ep1dJN3go+2+CR8LLX31qHrPVFqEHIXi9vfYMQWsjolhpIOvUnkB2KaSghjJ+Lr8jr9wXmy/js4uxl84j53b22hPWDYbE1EWvgkffRSGMnAJLNMqA2JVGjwji2EYQItnv4uBS0XEyiTWgCTX3mh7PhdgxSmXDVu2kmnJXeqKsyFMOWAMM6ZOYEygOMa0t3HygWO49YkOOjq7+sbz1QMajJEda0hLdQpkGYZRXXzLCd8tIkcAfxGRFxhY6ldV9fC0hTMcvunlpx291wC31yjFRMr7JHz0JVoqd1zCrKoWjMj4PlsZhlEIX++vC4GvAauBfwDNF+1WxxTjztrjYYSIi5T3ic4vNSYl0yKMe3sbe1x0V1XL+EJuCpW5izs4/+ancpbZWgS+NXW/qsljGM2Or03ly8BPgM8HWYGNCnLmdY/klL2dtMcoZp17aEGPpOm3L2NgzPdA2iNR4L7R+aXGpHT3atXL+AK0t+V+T4s1MYzK46tURgC3mEKpPFGFAvDwi2s487pHmHXuoXn3jTPAxxG9pydF50+/fdmARJGzH1vZMDVO4iZEFmtiGJXFdzH5d7hoeqPCRBVKofZSWBdRPoneZV3dOUb52Y+trNosIw06N/opWcMw0sN3pvJd4Hpxj353M9BQj6q+lKJcRgyF8nCNHJEZEKcSR5zXmM+yVqPMULJYGV/DqD6+SuXh4P1KXL36OFrLF8fIR/bGn7V5LHplTU7dkuP224lfP7oib7xHXKxJNM9XM2BlfA2jNvgqlU/SWOmbmp6u7h5mLVzRd1E6OruY/fjKAf1aW4Tthg1hXVd3YqLIW5/o4OQDxwworOUz6ylEsWlafBHcTCSpMJgZ4A2jNvjGqVxfYTmMgDFFeFhFb9Vx8Sk9vco2w4bw1GXOhTgpUeSdS15lm2H9P4fj9tspldlLpWwwZx4y1kr3GkYdUnbUl4i0iMioNIQxXABjWyZ3JbHcqI6wId7XKJ+dvYSj4GtJNralVYSzTKEYRt2Sr0b9GuBfVfXJ4LMAvwW+HDHKvx/4E2ZTKYlL5y7lxkdX9gUFnnHwrsyYOiHHlXfc29vK8v7aPhSv0e5pzO/q7uGWRav6EjzWsmbKNkNbWXbFMTU7vmEY/uRb/mqPbG8BjgemV1KgwcSlc5fm1CLpUe37HE5pMmnm/LKOE47XKGY1KpoxuFZs3NI8DgSG0exYOeEaMiuhuNWshStylnfKLdm7dmM34y6cVzGjeaUx12DDaBwsk14NSbq9R9vTuqnWu0JpwaXED2OuwYbRWJhSaQDijPfNSC+wzdAhOc4BM6ZOMNdgw2ggCi1/jRGR3YO/W0NtnaE+u6QvlhGNno8a719d1xUb5BiO36jveUk867q6+9yfDcNoPAoplTkxbXMjn32rxRoRzjpkbI6hPkuLDIyenzF1glc9EqXfyJ9NNd9ImP3EMBqbfErlnKpJMUg46ju/5y+vb+j7/K4dt+GsQ8bmuBQPGyJs7M71uorWmQfn0RWnL8KeXmccvGus0vKhtUU446Bdc6LUK+1WbPYTw2h8EpWKqv6ymoI0O1GFAvR9fnHGsX1t+erMh5fEkiYg4fasB1lYafnOXHp6lUdfepPX1m1CgdfWbfLaz5dMi3BaRGlZahXDaHzMpbhKRBVKUnvSjEDIXRLLR9QeE3ZPLqaMb1i2NJbRxoRydZkCMYzmxJRKnTF579GxS1bF3NKj9hjor3qYaYHuGsU0hm1ChmE0J3WrVETkCGBBzKZ1qtoe6jcSuAaYArQBjwBfUdWl1ZAzDcIzi41btqY6dld3DxfcvISvzH6KndvbOO2geOeASnPWIWOrfkzDMKpP3SqVEF8EHg997rvrBvnI7gDGAV/AFQ+7CFggIu9V1VVVlLNkfJe1SiW7dNXR2cWvF66gRchbcyVNsvnMLAGkYQwOGkGpPKuqCxO2nQhMAo5U1QUAIvIIsBz4Gk4hGSF6oSoO4GPa22y5yzAGIY0eUX8i8LesQgFQ1XW42cu/1UyqGCbtMXiqA5hrsGEMXhphpjJLRHYAOoF7gAtVNWsU2Bd4OmafZcDHRWRbVV1fJTkHsPcld7EppnBWszFyRIYRQ4eYZ5dhGHWtVNYB/w08CPwDOAC4GHhERA5Q1deBUcDLMftmi4+MBAYoFRE5DzgPYOzYyhiQkxTK8FbhuatdXEox7r31SlumlctO2NeUiGEYQB0rFVVdDCwONT0oIg8Bj+FsJZeWMfa1wLUAEydOrMhUImmGsqlHGX/hvIZNR9KWaWHUNsNsVmIYRix1q1TiUNUnReQFXLVJcN5eI2O6jgptrzuyJXsbjUyLMGPqfqZEDMNIpFEN9dlpwDKcXSXKPsCKWtpTmoVwGvprPrK/KRTDMPLSUDMVEZkI7EV/9uTbgXNE5HBVfTDo8zbgBODXtZGyuTC3YMMwiqFulYqIzMLFmzyJ8/w6ABfY2AH8b9DtdlwE/Q0iMo3+4EcBvl1tmStNNiFktcoCt4oU7mQYhhGibpUKzlX4DFyk/AjgNeA24DJVfQNAVXtF5Hjgv4AfAsNxSmayqq6sprDV8OTKKpJq1Ug54+Bdq3IcwzCaB9EGK+KUNhMnTtRFixaVNUatXINbAElx9hKeCVlqFcMw8iEiT6jqxGh7Pc9UmoKXZx4H5Fc8pS5r9dJf4xlg2JAWNm8tLQXxpD1GMevcQ0va1zAMI4splTqgnGWt8L49W0ubqZhCMQwjLUypDFKyMyjDMIw0MaVSYZohFYthGIYvjRr8WFc0WgGqwZQx2TCM6mIzlRKIm32cdchYbnx0ZVXjSErB7CeGYVQSUypFkrScdcPCFTl2inzLXgLs3N5Wkfxfw4a0sLVHzTXYMIyaYEqlBlQyoeSWrb0sNyO8YRg1wmwqTUajptQ3DKM5sJlKipTq6TWmva2vPkk5Mxgr42sYRq2xmUqFeXnmcQVjQjo6u/qWxIYUmcMxnJp+xtQJlpreMIyaYjOVInl55nEVjT3ZqjBE3HuW6Ocs5sllGEa9YUqlBKIzj7SVzFYdeIwzr3uEh19c0/fZFIphGPWIKZUqUe4MxxSIYRiNgCmVKlLpGY5hGEatMUN9CiQZ4gsZ6JPSpVgaFcMwGhUr0pVCka5yMFuJYRiNiBXpqlNMgRiG0UzY8pdhGIaRGqZUDMMwjNQwpWIYhmGkhikVwzAMIzVMqRiGYRipMehdikVkNfBKpHkH4I0aiGP0Y9eg9tg1qC31fv53U9XR0cZBr1TiEJFFcf7XRvWwa1B77BrUlkY9/7b8ZRiGYaSGKRXDMAwjNUypxHNtrQUw7BrUAXYNaktDnn+zqRiGYRipYTMVwzAMIzVMqRiGYRipYUolQER2FZE5IrJORP4hIreJyNhay9WMiMgpInKriLwiIl0i8ryIzBCR7SL9RorIT0XkDRHZICL3i8iEWsndzIjI3SKiInJVpN2uQQURkWNF5CERWR/cdxaJyJGh7Q13/k2pACIyApgP7A18AvgY8C5ggYhsU0vZmpSvAj3AxcAxwI+AzwL3iUgLgIgIcEew/QvAyUAGd012qYXQzYqInAHsH9Nu16CCiMhngN8CTwAnAR8BbgFGBNsb8/yr6qB/AV/C3eT2DLWNB7YC59davmZ7AaNj2j4OKHBk8Pnfgs+TQ322B9YA/1vr79AsL2Ak8BpwRnC+rwpts2tQufM+DugCvpynT0Oef5upOE4EFqrqX7MNqroceBh3YY0UUdXVMc2PB+9jgvcTgb+p6oLQfutwT252TdLjP4GnVfXGmG12DSrHJ4Fe4Md5+jTk+Tel4tgXeDqmfRmwT5VlGawcHrw/G7znuyZjRWTbqkjVxIjIB3EzxM8ldLFrUDk+CDwHnC4iL4rIVhH5q4iEr0VDnn9TKo5RwNqY9jW45QGjgojIGOAK4H5VXRQ057smYNelLERkKPAT4L9U9fmEbnYNKsfOOLvtNcBM4EPAfcD3ReRLQZ+GPP9Wo96oKcHT1m9x9qtzaizOYOJrQBtwda0FGaS0ANsBZ6vqbUHbfBEZB1wkIv9bK8HKxWYqjrXEa/2kJwUjBUSkDbc+vDtwtKquCm3Od02y240SCFzlLwG+AQwTkXYRaQ82Zz+3YtegkrwZvN8Xab8XeAewEw16/k2pOJbh1i+j7AM8U2VZBgUikgHmABOBY1V1aaRLvmuyQlXXV1jEZmZ3YDhwA+7GlH2Bc/deC0zArkElWVZgey8Nev5NqThuBw4Rkd2zDcE0dFKwzUiRIBZlFnAkMEVVF8Z0ux0YIyKHh/Z7G3ACdk3K5SlgcswLnKKZDPwVuwaV5DfB+9GR9mOAVar6Gg16/i2hJBAEOC7B+Y1fivMNvxK35rlfvT4RNCoi8iPg33Hr+XdGNq9S1VWB4vkjsCswDff0fBGwH7C/qq6sosiDAhFR4GpVvTT4bNegQgSBjQ/ggk4vAV7CBT9+GjhHVa9v2PNf60CZenkBY4FbgX8AbwFzgXG1lqsZX8DLOMUd95oe6jcK+DnO22UjwT9hreVv1heR4Ee7BhU/328DfgD8HdgC/Bn4aKOff5upGIZhGKlhNhXDMAwjNUypGIZhGKlhSsUwDMNIDVMqhmEYRmqYUjEMwzBSw5SKYRiGkRqmVJoEETlURG4SkVUisiUoTfq4iFwpIjvVSKYjghK1/1qh8a8XkZcrMXaeY7aIyFMi8tVqHrcYROTs4LzvWWtZCiEiF4vIiiD1+1MVOkb2d3hEJcb3lGGKiPy9XtPVp4kplSZARC7AFRQbjcsI8K/A6cA9wHm44Klm5EpcGdZqchYu2d8Pq3zcpkNEDsJlVbgJOAxXxrtZ+S3wKi4yvqmx1PcNjohMxtVk+J6qfiWy+S4RmYFL/5BvjAywVRssElZVX6zBYb8K/EpVN9bg2HWDiAxT1c1lDvPu4P3HqvpSuTLVM6qqInItcKWIzFDVTbWWqVLYTKXx+TrwRvA+AFXdoKrXZz+LyLhgKeA/ROTbIvI3YDPQLiKjReQnIvKCiGwUkZUi8uugiBahMaYHY0wQkQVB31dF5IogX1GUESLyfRF5I3jdEEq1noiIfFREFovI+mA5b6mIfCa0PWf5S0ReDuQa8IqMO1VEFgZyd4rILUE6+ELyHIzL3vvrhPPxLhGZF8j7ioh8M3w+QstS4+L2j7SpiFwlIhcEY20Mxt4xeN0sIuuCaxR77YGdRWRuIM+bIvIDceUGwscZISL/KSLLg2XT5SJySUTu7PLRVBG5TkRW41KL5DtXB4nI/cGxN4jIA8HMJLv998D1wccXg/GnFxjzPBFZIiKbgt/Rz0RkVKTP6OA3+4/g2v4KGPBbE5HW4Py+Gpzb+SKyd5wcIrK/iNwuImtFpEtEHhaRf470eb+I3Bec5y4ReUlEorPZmwNZpub7no2OKZUGRkSG4Mrw3qeqW4rc/RLgn3DLYycBm3B5hjbhktYdg5uqvwt4WESGx4wxF7gfmIK70X4D+GZMv+/h8kp9FLgcODloy/fdPojLmPtgMP4pwHXE3CBCnAQcGnodBazGlW3NjvvvuBxvzwRjfgZ4D/CgiGyXTybcOXkLl3w0jt8A8wN55+K+6ycKjJmPj+EyOf8H8Hngn4FfBcf5M+483gXMFJFjY/a/AZdteCrwP8C5wI+yG4Pfzz24JIbfAz4M/BR3Ha+JGe//AAnkOjtJaBHZD3fdRgb9Po7Lc/WgiOwfdPsPYEbw91Tc9fppnjFn4vJk3Y+r3T4Ndz1+J672S5bbgOOBi4HTcMXf/i9myMuDPr/C1Xu/l5jMvyLyPuBPuP+Nc3Hn/E3gfhE5MOizLe489gTf98O4SqY5K0Gq+gauXPYxSd+zKah18jF7lf7CFfNRYEbMtiHhV6h9XLDPkwRZqvOM34rLkKrASaH26UHbhZH+1+Fuuu3B5yOCfr+M9Ps+TnklHh+3zLSmgHzXAy8nbGvB3SRWA7sHbdsC64CfR/qOxyX0+3KB4/0OeDimPXs+zom0LwXuDX0+O+g3Lm7/SJsCL0Su3XeC9ksj1/l14Bcxx/lxZMxLcDe+fwo+fyzod1hMvy3AjpHr+BvP3+UcoDP7Owja3oZLinhbqO3TcecjZrxxgdzfjLRPCvafEnw+Kvh8esx1U+CI4PNIYD3ww0i/8xmY1PQBnCIYGvm/eBaYG3yeGOy3n8e5+X/ACz7nsVFfNlNpQkTknUB3+BU8lYaZq8GvPLLvZ4MlhvW4p7wVwaa9Yg51c+TzTbgb93si7fMin5cCw3BKMYnHgZHilsqOF4/lsgj/iav7PUX71+sPxd3cZonIkOwLWImbzRxWYMydcUoqiej3fBqX/bpU7lPVraHP2RnXPdmGYPtfcco/Stz1aQGyy1DHAK8Af4qcj3uBDHBIZP/f4MdhwJ2q2hmS8x84JX944l7JHBXIHb1uj+IeYrLX7VCc8rk1sv9Nkc8TgG2AWyLtc8IfgqXCw4N+vaHjCm7GlD3uX3BK9CcicpaIxF2LLKtxv6OmxZRKY/Mm7ok/euN6A3h/8LouYd9Xow0i8gWcV9P9uCWJg+i/scQtf0XX1bOfx0Ta10Q+Zw28cWMCoKoP4hwMdsXdzFYHa/T7Je2TRUQ+BVwAfFJVHw5t2jF4v5+I0sXdaN5eYOjhIdnjiPueid/Rg2i52C152ku5PjsCuzHwXDwWbI+ejwG/mQRGJfR9jfjyuIXIXre/MlDW7eiXcydgrap2R/aPnoesi/3rBfqNws1KvhFz3M/jHnpaVHUdrrDZ33D/PytE5GkROTnmu3RR3m+i7jHvrwZGVbeKyEPAUSIyVAO7SvD0ughARI5P2j2m7XTgAVW9INsgIuPziPAOXHGh8GeADs+vkBdVnQPMCdasj8DNPu4WkV1UtTduH3FV8n4EXKGqv45sztYFP5v4cq5vFRDpTUq7KWbJevwMjbQXUmal8g5yv2f0+rwJLAdOTdj/5chnX+/ANcA7Y9rfSWl11bPX7UMJ+2e3v4q70WciiiU6I84qvB2JPz9ZOnFlfX+As70MIPs7VNWngJODmcxEnF3yZhHZX1WfDu0yKiRvU2JKpfH5NnAf7oYbdSkulhG4ImVhzsnT/1RgZujz6bi16mi9+bJQV3nzTnHlnr+HuwkPWIYSF+x3G3CLqk6PGepPOMWxp6r+sgRRfJbI8vFK8P4enL0kayz/UBlj5uNUnONAltNxN8lHg8934wzP61X1OdLjQeBYEdlOVd8CCJwgTgB+X8J49+HkHquq9+Xp9whuZnEyuUtep0f6LQU24GbCC0LtOa73qrpBRP6Aq874ZNKDTGSfrcBCEfkGzqHg3bhl0CzjgecLjdPImFJpcFT1ARG5EOcBtB/uiWo5bor9T7h/qA34PWXeDXxdRC7GLYEcifOQSuLcwPX0cVyt7U/jjJzrSv0+WUTkCtyT4wLcssIuwBeBp1Q1ya5xJ6463rUikmMPUNWFqvoPEZkG/EBERuMMuOtwy0GHA7+Pmd2EeQg4R0TerqqlPG0+DrwIXBOct804L6hhJYzlw7Eicg3ORnIQcBkuxuYvwfZZuIeGB0Tkv3FebUOBPXA3xClaWjzOlTgPrAdE5D9xv72v4x5arih2MFV9MRjn+yKyF05pbcItjR4F/FRVF6jqfSLyR5xtYwecreM0IjY+VV0rIt8FLhaRt3DLoe8DPhV0CSuP83HX/R4R+RlulrND0L9VVS8MVgPOw3n8LcfZa76Ie4B5JDuQiAjuOjR34GytPQXslc4L5wlzM25pYwtuxvE4znVyp1C/cbh/8k/HjNGGWzpajfuHuBP3ZBX1iJketL0Hd9Pvwq2XXwm0hPodEfT718hxzqaA1w9wHM4g/Sru5rsS+Bmwc6jP9YS8v0guUayRsY8N5P4HTgn9BZd1YJ8C53hk8F0/EWnPno8hkfYc+YK2fXFP6+txThDnk+z9FS3tmz1ve0bafw/8MabfYbhI7vW4JakfAG2RfYcHx38uOM9rgt/N9Oz3SbqOBc7Vwbib9XrcQ80DwEGRPl7eX6H+HwMWBuOtx3lgfR/YJdRnNHAj7vfbSb/LcJ/3V9CvFRfN/1pwTX8PfCDo96XIcd+Nm/m8HpyjVTing2OD7XsBs3EKZRPu/+cu4OCY/1EF3lPr+0UlX1ZO2CiaIDjsMiCjud5JTY+IXI+7iVUkn5lRO0TkFJyn12Gq+ocKjP8jnEL554KdGxhb/jKM4rgceFZEJqrqoloLY5SGuOwIx+HsS5uAA4ELcTOhP1bgeO/EBcI2d+AjplQMoyhUdbmInE2/m6vRmKzHLQ9+Dhe79Dpu+fgirczyzTjgAlV9qAJj1xW2/GUYhmGkhgU/GoZhGKlhSsUwDMNIDVMqhmEYRmqYUjEMwzBSw5SKYRiGkRr/H7Fn49U1OI0bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot norm vs number of edges\n",
    "xs, ys = [len(m.edges) for m in motifs], [np.linalg.norm(emb) for emb in embs]\n",
    "plt.scatter(xs, ys)\n",
    "plt.xlabel(\"Graph size (number of edges)\")\n",
    "plt.ylabel(\"Embedding norm\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of node size vs edge size vs embedding norm\n",
    "a = defaultdict(lambda: defaultdict(list))\n",
    "for m, emb in zip(motifs, embs):\n",
    "    a[len(m)][len(m.edges)].append(np.linalg.norm(emb))\n",
    "a_mat = np.zeros((max_n_edges, max_n_nodes))\n",
    "for i in range(max_n_nodes):\n",
    "    for j in range(max_n_edges):\n",
    "        a_mat[j][i] = np.mean(a[i][j] if a[i][j] else 0)\n",
    "ax = sns.heatmap(a_mat, linewidth=0)\n",
    "ax.invert_yaxis()\n",
    "plt.xlabel(\"Number of nodes in graph\")\n",
    "plt.ylabel(\"Number of edges in graph\")\n",
    "plt.title(\"Average embedding norm\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
